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Mathematics
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Machine Learning and Deep Learning for Healthcare Applications and Advances
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Machine Learning and Deep Learning for Healthcare Applications and Advances

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (10 September 2023) | Viewed by 28126

Special Issue Editors

Prof. Dr. Xujuan Zhou

E-Mail Website
Guest Editor
School of Management and Enterprise, University of Southern Queensland, Toowoomba, Australia
Interests: data and text mining; machine and deep learning; health informatics; business analytics; information retrieval/filtering; recommender systems; sentiment analysis; natural language processing; information systems and management
Special Issues, Collections and Topics in MDPI journals
Dr. Lemai Nguyen

E-Mail Website
Guest Editor
Deakin Business School, Deakin University, Melbourne, Australia
Interests: information systems; health informatics; requirements engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Guohun Zhu

E-Mail Website
Guest Editor
School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
Interests: imaging; sensing and biomedical engineering

Special Issue Information

Dear Colleagues,

Machine learning and deep learning are artificial intelligence techniques to solve complex problems and build intelligent systems. Machine learning and deep learning have found their application in many domains, including healthcare and medical research. They have been increasingly used to develop computer-aided disease detection and computer-aided diagnosis systems to help healthcare professionals to make more accurate diagnoses, plan, and deliver better-quality and safer treatments, and ultimately lead to better healthcare outcomes. Other applications include personalized medicines, drug discovery, medical imaging diagnosis, outbreak prediction, mental health, physiological signal processing, health informatics, and smart health records. Although progress is being achieved in developing and applying machine learning and deep learning algorithms in healthcare, many opportunities are to be explored and challenges to be overcome.

The primary aim of this Special Issue is to bring together original research presenting and discussing innovative efforts in applications and advances of machine learning and deep learning in healthcare. This Special Issue will cover various topics including but not limited to the development of new algorithms, development of innovative technologies, and application and deployment of machine learning and deep learning algorithms and technologies in various healthcare settings. Both research and review papers addressing these topics are invited.

Dr. Xujuan Zhou
Dr. Lemai Nguyen
Dr. Guohun Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • transfer learning
  • algorithms
  • biomedical application
  • biomedical image analysis and processing
  • data analytics and visualization
  • forecasting
  • healthcare
  • health informatics
  • signal processing

Published Papers (16 papers)

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Research

23 pages, 1757 KiB  
Article
DB-COVIDNet: A Defense Method against Backdoor Attacks
by Samaneh Shamshiri, Ki Jin Han and Insoo Sohn
Mathematics 2023, 11(20), 4236; https://doi.org/10.3390/math11204236 - 10 Oct 2023
Cited by 2 | Viewed by 867
Abstract
With the emergence of COVID-19 disease in 2019, machine learning (ML) techniques, specifically deep learning networks (DNNs), played a key role in diagnosing the disease in the medical industry due to their superior performance. However, the computational cost of deep learning networks (DNNs) [...] Read more.
With the emergence of COVID-19 disease in 2019, machine learning (ML) techniques, specifically deep learning networks (DNNs), played a key role in diagnosing the disease in the medical industry due to their superior performance. However, the computational cost of deep learning networks (DNNs) can be quite high, making it necessary to often outsource the training process to third-party providers, such as machine learning as a service (MLaaS). Therefore, careful consideration is required to achieve robustness in DNN-based systems against cyber-security attacks. In this paper, we propose a method called the dropout-bagging (DB-COVIDNet) algorithm, which works as a robust defense mechanism against poisoning backdoor attacks. In this model, the trigger-related features will be removed by the modified dropout algorithm, and then we will use the new voting method in the bagging algorithm to achieve the final results. We considered AC-COVIDNet as the main inducer of the bagging algorithm, which is an attention-guided contrastive convolutional neural network (CNN), and evaluated the performance of the proposed method with the malicious COVIDx dataset. The results demonstrated that DB-COVIDNet has strong robustness and can significantly reduce the effect of the backdoor attack. The proposed DB-COVIDNet nullifies backdoors before the attack has been activated, resulting in a tremendous reduction in the attack success rate from 99.5% to 3% with high accuracy on the clean data. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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29 pages, 4218 KiB  
Article
Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning
by Dong-Her Shih, Pai-Ling Shih, Ting-Wei Wu, Chen-Xuan Lee and Ming-Hung Shih
Mathematics 2023, 11(19), 4118; https://doi.org/10.3390/math11194118 - 28 Sep 2023
Viewed by 3438
Abstract
Urinary tract cancers are considered life-threatening conditions worldwide, and Bladder Cancer is one of the most malignant urinary tract tumors, with an estimated number of more than 1.3 million cases worldwide each year. Bladder Cancer is a heterogeneous disease; the main symptom is [...] Read more.
Urinary tract cancers are considered life-threatening conditions worldwide, and Bladder Cancer is one of the most malignant urinary tract tumors, with an estimated number of more than 1.3 million cases worldwide each year. Bladder Cancer is a heterogeneous disease; the main symptom is painless hematuria. However, patients with Bladder Cancer may initially be misdiagnosed as Cystitis or infection, and cystoscopy alone may sometimes be misdiagnosed as urolithiasis or Cystitis, thereby delaying medical attention. Early diagnosis of Bladder Cancer is the key to successful treatment. This study uses six deep learning methods through different oversampling techniques and feature selection, and then through dimensionality reduction techniques, to establish a set that can effectively distinguish between Bladder Cancer and Cystitis patient’s deep learning model. The research results show that based on the laboratory clinical dataset, the deep learning model proposed in this study has an accuracy rate of 89.03% in distinguishing between Bladder Cancer and Cystitis, surpassing the results of previous studies. The research model developed in this study can be provided to clinicians as a reference to differentiate between Bladder Cancer and Cystitis. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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19 pages, 2302 KiB  
Article
HGSOXGB: Hunger-Games-Search-Optimization-Based Framework to Predict the Need for ICU Admission for COVID-19 Patients Using eXtreme Gradient Boosting
by Farhana Tazmim Pinki, Md Abdul Awal, Khondoker Mirazul Mumenin, Md. Shahadat Hossain, Jabed Al Faysal, Rajib Rana, Latifah Almuqren, Amel Ksibi and Md Abdus Samad
Mathematics 2023, 11(18), 3960; https://doi.org/10.3390/math11183960 - 18 Sep 2023
Viewed by 819
Abstract
Millions of people died in the COVID-19 pandemic, which pressured hospitals and healthcare workers into keeping up with the speed and intensity of the outbreak, resulting in a scarcity of ICU beds for COVID-19 patients. Therefore, researchers have developed machine learning (ML) algorithms [...] Read more.
Millions of people died in the COVID-19 pandemic, which pressured hospitals and healthcare workers into keeping up with the speed and intensity of the outbreak, resulting in a scarcity of ICU beds for COVID-19 patients. Therefore, researchers have developed machine learning (ML) algorithms to assist in identifying patients at increased risk of requiring an ICU bed. However, many of these studies used state-of-the-art ML algorithms with arbitrary or default hyperparameters to control the learning process. Hyperparameter optimization is essential in enhancing the classification effectiveness and ensuring the optimal use of ML algorithms. Therefore, this study utilized an improved Hunger Games Search Optimization (HGSO) algorithm coupled with a robust extreme gradient boosting (XGB) classifier to predict a COVID-19 patient’s need for ICU transfer. To further mitigate the random initialization inherent in HGSO and facilitate an efficient convergence toward optimal solutions, the Metropolis–Hastings (MH) method is proposed for integration with HGSO. In addition, population diversity was reintroduced to effectively escape local optima. To evaluate the efficacy of the MH-based HGSO algorithm, the proposed method was compared with the original HGSO algorithm using the Congress on Evolutionary Computation benchmark function. The analysis revealed that the proposed algorithm converges better than the original method and exhibits statistical significance. Consequently, the proposed algorithm optimizes the XGB hyperparameters to further predict the need for ICU transfer for COVID-19 patients. Various evaluation metrics, including the receiver operating curve (ROC), precision–recall curve, bootstrap ROC, and recall vs. decision boundary, were used to estimate the effectiveness of the proposed HGSOXGB model. The model achieves the highest accuracy of 97.39% and an area under the ROC curve of 99.10% compared with other classifiers. Additionally, the important features that significantly affect the prediction of ICU transfer need using XGB were calculated. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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14 pages, 1916 KiB  
Article
Self-Supervised Skin Lesion Segmentation: An Annotation-Free Approach
by Abdulrahman Gharawi, Mohammad D. Alahmadi and Lakshmish Ramaswamy
Mathematics 2023, 11(18), 3805; https://doi.org/10.3390/math11183805 - 05 Sep 2023
Viewed by 860
Abstract
Skin cancer poses a significant health risk, affecting multiple layers of the skin, including the dermis, epidermis, and hypodermis. Melanoma, a severe type of skin cancer, originates from the abnormal proliferation of melanocytes in the epidermis. Current methods for skin lesion segmentation heavily [...] Read more.
Skin cancer poses a significant health risk, affecting multiple layers of the skin, including the dermis, epidermis, and hypodermis. Melanoma, a severe type of skin cancer, originates from the abnormal proliferation of melanocytes in the epidermis. Current methods for skin lesion segmentation heavily rely on large annotated datasets, which are costly, time-consuming, and demand specialized expertise from dermatologists. To address these limitations and improve logistics in dermatology practices, we present a self-supervised strategy for accurate skin lesion segmentation in dermatologist images, eliminating the need for manual annotations. Unlike the traditional appraoch, our proposed approach integrates a hybrid CNN/Transformer model, harnessing the complementary strengths of both architectures. The Transformer module captures long-range contextual dependencies, enabling a comprehensive understanding of image content, while the CNN encoder extracts local semantic information. To dynamically recalibrate the representation space, we introduce a contextual attention module that effectively combines hierarchical features and pixel-level information. By incorporating local and global dependencies among image pixels, we perform a clustering process that organizes the image content into a meaningful space. Furthermore, as another contribution, we incorporate a spatial consistency loss to promote the gradual merging of clusters with similar representations, thereby improving the segmentation quality. Experimental evaluations conducted on two publicly available skin lesion segmentation datasets demonstrate the superiority of our proposed method, outperforming both unsupervised and self-supervised strategies, and achieving state-of-the-art performance in this challenging task. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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21 pages, 3994 KiB  
Article
Deep Learning-Based Classification of Abrasion and Ischemic Diabetic Foot Sores Using Camera-Captured Images
by Mudassir Khalil, Ahmad Naeem, Rizwan Ali Naqvi, Kiran Zahra, Syed Atif Moqurrab and Seung-Won Lee
Mathematics 2023, 11(17), 3793; https://doi.org/10.3390/math11173793 - 04 Sep 2023
Cited by 1 | Viewed by 1353
Abstract
Diabetic foot sores (DFS) are serious diabetic complications. The patient’s weakened neurological system damages the tissues of the foot’s skin, which results in amputation. This study aims to validate and deploy a deep learning-based system for the automatic classification of abrasion foot sores [...] Read more.
Diabetic foot sores (DFS) are serious diabetic complications. The patient’s weakened neurological system damages the tissues of the foot’s skin, which results in amputation. This study aims to validate and deploy a deep learning-based system for the automatic classification of abrasion foot sores (AFS) and ischemic diabetic foot sores (DFS). We proposed a novel model combining convolutional neural network (CNN) capabilities with Vgg-19. The proposed method utilized two benchmark datasets to classify AFS and DFS from the patient’s foot. A data augmentation technique was used to enhance the accuracy of the training. Moreover, image segmentation was performed using UNet++. We tested and evaluated the proposed model’s classification performance against two well-known pre-trained classifiers, Inceptionv3 and MobileNet. The proposed model classified AFS and ischemia DFS images with an accuracy of 99.05%, precision of 98.99%, recall of 99.01%, MCC of 0.9801, and f1 score of 99.04%. Furthermore, the results of statistical evaluations using ANOVA and Friedman tests revealed that the proposed model exhibited a remarkable performance. The proposed model achieved an excellent performance that assist medical professionals in identifying foot ulcers. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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26 pages, 2644 KiB  
Article
Efficient Harris Hawk Optimization (HHO)-Based Framework for Accurate Skin Cancer Prediction
by Walaa N. Ismail and Hessah A. Alsalamah
Mathematics 2023, 11(16), 3601; https://doi.org/10.3390/math11163601 - 20 Aug 2023
Cited by 2 | Viewed by 1126
Abstract
The prediction of skin cancer poses a number of challenges due to the differences in visual characteristics between melanoma, basal cell carcinomas, and squamous cell carcinomas. These visual differences pose difficulties for models in discerning subtle features and patterns accurately. However, a remarkable [...] Read more.
The prediction of skin cancer poses a number of challenges due to the differences in visual characteristics between melanoma, basal cell carcinomas, and squamous cell carcinomas. These visual differences pose difficulties for models in discerning subtle features and patterns accurately. However, a remarkable breakthrough in image analysis using convolutional neural networks (CNNs) has emerged, specifically in the identification of skin cancer from images. Unfortunately, manually designing such neural architectures is prone to errors and consumes substantial time. It has become increasingly popular to design and fine-tune neural networks by using metaheuristic algorithms that are based on natural phenomena. A nature-inspired algorithm is a powerful alternative to traditional algorithms for solving problems, particularly in complex optimization tasks. One such algorithm, the Harris hawk optimization (HHO), has demonstrated promise in automatically identifying the most appropriate solution across a wide range of possibilities, making it suitable for solving complex optimization problems. The purpose of this study is to introduce a novel automated architecture called “HHOForSkin” that combines the power of convolutional neural networks with meta-heuristic optimization techniques. The HHOForSkin framework uses an innovative custom CNN architecture with 26 layers for the analysis of medical images. In addition, a Harris hawk optimization algorithm (HHO) is used to fine-tune the developed model for multiple skin cancer classification problems. The developed model achieves an average accuracy of 99.1% and 98.93% F1 score using a publicly available skin cancer dataset. These results position the developed optimization-based skin cancer detection strategy at the forefront, offering the highest accuracy for seven-class classification problems compared to related works. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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17 pages, 3579 KiB  
Article
Enhancing Robustness of Viewpoint Changes in 3D Skeleton-Based Human Action Recognition
by Jinyoon Park, Chulwoong Kim and Seung-Chan Kim
Mathematics 2023, 11(15), 3280; https://doi.org/10.3390/math11153280 - 26 Jul 2023
Cited by 1 | Viewed by 967
Abstract
Previous research on 3D skeleton-based human action recognition has frequently relied on a sequence-wise viewpoint normalization process, which adjusts the view directions of all segmented action sequences. This type of approach typically demonstrates robustness against variations in viewpoint found in short-term videos, a [...] Read more.
Previous research on 3D skeleton-based human action recognition has frequently relied on a sequence-wise viewpoint normalization process, which adjusts the view directions of all segmented action sequences. This type of approach typically demonstrates robustness against variations in viewpoint found in short-term videos, a characteristic commonly encountered in public datasets. However, our preliminary investigation of complex action sequences, such as discussions or smoking, reveals its limitations in capturing the intricacies of such actions. To address these view-dependency issues, we propose a straightforward, yet effective, sequence-wise augmentation technique. This strategy enhances the robustness of action recognition models, particularly against changes in viewing direction that mainly occur within the horizontal plane (azimuth) by rotating human key points around either the z-axis or the spine vector, effectively creating variations in viewing directions. We scrutinize the robustness of this approach against real-world viewpoint variations through extensive empirical studies on multiple public datasets, including an additional set of custom action sequences. Despite the simplicity of our approach, our experimental results consistently yield improved action recognition accuracies. Compared to the sequence-wise viewpoint normalization method used with advanced deep learning models like Conv1D, LSTM, and Transformer, our approach showed a relative increase in accuracy of 34.42% for the z-axis and 10.86% for the spine vector. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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22 pages, 2908 KiB  
Article
Transformer Models and Convolutional Networks with Different Activation Functions for Swallow Classification Using Depth Video Data
by Derek Ka-Hei Lai, Ethan Shiu-Wang Cheng, Bryan Pak-Hei So, Ye-Jiao Mao, Sophia Ming-Yan Cheung, Daphne Sze Ki Cheung, Duo Wai-Chi Wong and James Chung-Wai Cheung
Mathematics 2023, 11(14), 3081; https://doi.org/10.3390/math11143081 - 12 Jul 2023
Cited by 1 | Viewed by 1150
Abstract
Dysphagia is a common geriatric syndrome that might induce serious complications and death. Standard diagnostics using the Videofluoroscopic Swallowing Study (VFSS) or Fiberoptic Evaluation of Swallowing (FEES) are expensive and expose patients to risks, while bedside screening is subjective and might lack reliability. [...] Read more.
Dysphagia is a common geriatric syndrome that might induce serious complications and death. Standard diagnostics using the Videofluoroscopic Swallowing Study (VFSS) or Fiberoptic Evaluation of Swallowing (FEES) are expensive and expose patients to risks, while bedside screening is subjective and might lack reliability. An affordable and accessible instrumented screening is necessary. This study aimed to evaluate the classification performance of Transformer models and convolutional networks in identifying swallowing and non-swallowing tasks through depth video data. Different activation functions (ReLU, LeakyReLU, GELU, ELU, SiLU, and GLU) were then evaluated on the best-performing model. Sixty-five healthy participants (n = 65) were invited to perform swallowing (eating a cracker and drinking water) and non-swallowing tasks (a deep breath and pronouncing vowels: “/eɪ/”, “/iː/”, “/aɪ/”, “/oʊ/”, “/u:/”). Swallowing and non-swallowing were classified by Transformer models (TimeSFormer, Video Vision Transformer (ViViT)), and convolutional neural networks (SlowFast, X3D, and R(2+1)D), respectively. In general, convolutional neural networks outperformed the Transformer models. X3D was the best model with good-to-excellent performance (F1-score: 0.920; adjusted F1-score: 0.885) in classifying swallowing and non-swallowing conditions. Moreover, X3D with its default activation function (ReLU) produced the best results, although LeakyReLU performed better in deep breathing and pronouncing “/aɪ/” tasks. Future studies shall consider collecting more data for pretraining and developing a hyperparameter tuning strategy for activation functions and the high dimensionality video data for Transformer models. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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17 pages, 845 KiB  
Article
Automated EEG Pathology Detection Based on Significant Feature Extraction and Selection
by Yunning Zhong, Hongyu Wei, Lifei Chen and Tao Wu
Mathematics 2023, 11(7), 1619; https://doi.org/10.3390/math11071619 - 27 Mar 2023
Cited by 2 | Viewed by 1610
Abstract
Neurological diseases are a significant health threat, often presenting through abnormalities in electroencephalogram (EEG) signals during seizures. In recent years, machine learning (ML) technologies have been explored as a means of automated EEG pathology diagnosis. However, existing ML-based EEG binary classification methods largely [...] Read more.
Neurological diseases are a significant health threat, often presenting through abnormalities in electroencephalogram (EEG) signals during seizures. In recent years, machine learning (ML) technologies have been explored as a means of automated EEG pathology diagnosis. However, existing ML-based EEG binary classification methods largely focus on extracting EEG-related features, which may lead to poor performance in classifying EEG signals by overlooking potentially redundant information. In this paper, we propose a novel Kruskal–Wallis (KW) test-based framework for EEG pathology detection. Our framework first divides EEG data into frequency sub-bands using wavelet packet decomposition and then extracts statistical characteristics from each selected coefficient. Next, the piecewise aggregation approximation technique is used to obtain the aggregated feature vectors, followed by the KW statistical test methodology to select significant features. Finally, three ensemble learning classifiers, random forest, categorical boosting (CatBoost), and light gradient boosting machine, are used to classify the extracted significant features into normal or abnormal classes. Our proposed framework achieves an accuracy of 89.13%, F1-score of 87.60%, and G-mean of 88.60%, respectively, outperforming other competing techniques on the same dataset, which shows the great promise in EEG pathology detection. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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25 pages, 4945 KiB  
Article
CNN and Bidirectional GRU-Based Heartbeat Sound Classification Architecture for Elderly People
by Harshwardhan Yadav, Param Shah, Neel Gandhi, Tarjni Vyas, Anuja Nair, Shivani Desai, Lata Gohil, Sudeep Tanwar, Ravi Sharma, Verdes Marina and Maria Simona Raboaca
Mathematics 2023, 11(6), 1365; https://doi.org/10.3390/math11061365 - 10 Mar 2023
Cited by 3 | Viewed by 2706
Abstract
Cardiovascular diseases (CVDs) are a significant cause of death worldwide. CVDs can be prevented by diagnosing heartbeat sounds and other conventional techniques early to reduce the harmful effects caused by CVDs. However, it is still challenging to segment, extract features, and predict heartbeat [...] Read more.
Cardiovascular diseases (CVDs) are a significant cause of death worldwide. CVDs can be prevented by diagnosing heartbeat sounds and other conventional techniques early to reduce the harmful effects caused by CVDs. However, it is still challenging to segment, extract features, and predict heartbeat sounds in elderly people. The inception of deep learning (DL) algorithms has helped detect various types of heartbeat sounds at an early stage. Motivated by this, we proposed an intelligent architecture categorizing heartbeat into normal and murmurs for elderly people. We have used a standard heartbeat dataset with heartbeat class labels, i.e., normal and murmur. Furthermore, it is augmented and preprocessed by normalization and standardization to significantly reduce computational power and time. The proposed convolutional neural network and bi-directional gated recurrent unit (CNN + BiGRU) attention-based architecture for the classification of heartbeat sound achieves an accuracy of 90% compared to the baseline approaches. Hence, the proposed novel CNN + BiGRU attention-based architecture is superior to other DL models for heartbeat sound classification. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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23 pages, 7649 KiB  
Article
Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture
by Jorge Brieva, Hiram Ponce and Ernesto Moya-Albor
Mathematics 2023, 11(3), 645; https://doi.org/10.3390/math11030645 - 27 Jan 2023
Cited by 3 | Viewed by 2344
Abstract
The breathing rate monitoring is an important measure in medical applications and daily physical activities. The contact sensors have shown their effectiveness for breathing monitoring and have been mostly used as a standard reference, but with some disadvantages for example in burns patients [...] Read more.
The breathing rate monitoring is an important measure in medical applications and daily physical activities. The contact sensors have shown their effectiveness for breathing monitoring and have been mostly used as a standard reference, but with some disadvantages for example in burns patients with vulnerable skins. Contactless monitoring systems are then gaining attention for respiratory frequency detection. We propose a new non-contact technique to estimate the breathing rate based on the motion video magnification method by means of the Hermite transform and an Artificial Hydrocarbon Network (AHN). The chest movements are tracked by the system without the use of an ROI in the image video. The machine learning system classifies the frames as inhalation or exhalation using a Bayesian-optimized AHN. The method was compared using an optimized Convolutional Neural Network (CNN). This proposal has been tested on a Data-Set containing ten healthy subjects in four positions. The percentage error and the Bland–Altman analysis is used to compare the performance of the strategies estimating the breathing rate. Besides, the Bland–Altman analysis is used to search for the agreement of the estimation to the reference.The percentage error for the AHN method is 2.19±2.1 with and agreement with respect of the reference of ≈99%. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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20 pages, 917 KiB  
Article
A Trustworthy Healthcare Management Framework Using Amalgamation of AI and Blockchain Network
by Dhairya Jadav, Nilesh Kumar Jadav, Rajesh Gupta, Sudeep Tanwar, Osama Alfarraj, Amr Tolba, Maria Simona Raboaca and Verdes Marina
Mathematics 2023, 11(3), 637; https://doi.org/10.3390/math11030637 - 27 Jan 2023
Cited by 4 | Viewed by 1823
Abstract
Over the last few decades, the healthcare industry has continuously grown, with hundreds of thousands of patients obtaining treatment remotely using smart devices. Data security becomes a prime concern with such a massive increase in the number of patients. Numerous attacks on healthcare [...] Read more.
Over the last few decades, the healthcare industry has continuously grown, with hundreds of thousands of patients obtaining treatment remotely using smart devices. Data security becomes a prime concern with such a massive increase in the number of patients. Numerous attacks on healthcare data have recently been identified that can put the patient’s identity at stake. For example, the private data of millions of patients have been published online, posing a severe risk to patients’ data privacy. However, with the advent of Industry 4.0, medical practitioners can digitally assess the patient’s condition and administer prompt prescriptions. However, wearable devices are also vulnerable to numerous security threats, such as session hijacking, data manipulation, and spoofing attacks. Attackers can tamper with the patient’s wearable device and relays the tampered data to the concerned doctor. This can put the patient’s life at high risk. Since blockchain is a transparent and immutable decentralized system, it can be utilized for securely storing patient’s wearable data. Artificial Intelligence (AI), on the other hand, utilizes different machine learning techniques to classify malicious data from an oncoming stream of patient’s wearable data. An amalgamation of these two technologies would make the possibility of tampering the patient’s data extremely difficult. To mitigate the aforementioned issues, this paper proposes a blockchain and AI-envisioned secure and trusted framework (HEART). Here, Long-Short Term Model (LSTM) is used to classify wearable devices as malicious or non-malicious. Then, we design a smart contract that allows only of those patients’ data having a wearable device to be classified as non-malicious to the public blockchain network. This information is then accessible to all involved in the patient’s care. We then evaluate the HEART’s performance considering various evaluation metrics such as accuracy, recall, precision, scalability, and network latency. On the training and testing sets, the model achieves accuracies of 93% and 92.92%, respectively. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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9 pages, 16531 KiB  
Communication
Stroke Localization Using Multiple Ridge Regression Predictors Based on Electromagnetic Signals
by Shang Gao, Guohun Zhu, Alina Bialkowski and Xujuan Zhou
Mathematics 2023, 11(2), 464; https://doi.org/10.3390/math11020464 - 15 Jan 2023
Cited by 2 | Viewed by 1076
Abstract
Localizing stroke may be critical for elucidating underlying pathophysiology. This study proposes a ridge regression–meanshift (RRMS) framework using electromagnetic signals obtained from 16 antennas placed around the anthropomorphic head phantom. A total of 608 intracranial haemorrhage (ICH) and ischemic (IS) signals are collected [...] Read more.
Localizing stroke may be critical for elucidating underlying pathophysiology. This study proposes a ridge regression–meanshift (RRMS) framework using electromagnetic signals obtained from 16 antennas placed around the anthropomorphic head phantom. A total of 608 intracranial haemorrhage (ICH) and ischemic (IS) signals are collected and evaluated for RRMS, where each type of signal contains two different diameters of stroke phantoms. Subsequently, multiple ridge regression predictors then give the target distances from the antennas and mean shift is used to cluster the predicted stroke location based on these distances. The test results show that the training time and economic cost are significantly reduced as the average prediction time only takes 0.61 s to achieve an accurate result (average position error = 0.74 cm) using a conventional laptop. It has great potential to be used as an auxiliary standard medical method, or rapid diagnosis of stroke patients in underdeveloped areas, due to its rapidity, good deployability, and low hardware cost. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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26 pages, 520 KiB  
Article
Predicting Women with Postpartum Depression Symptoms Using Machine Learning Techniques
by Abinaya Gopalakrishnan, Revathi Venkataraman, Raj Gururajan, Xujuan Zhou and Guohun Zhu
Mathematics 2022, 10(23), 4570; https://doi.org/10.3390/math10234570 - 02 Dec 2022
Cited by 4 | Viewed by 2778
Abstract
Being pregnant and giving birth are big life stages that occur for women. The physical and mental effects of pregnancy and childbirth, like those of many other fleeting life experiences, have the significant potential to influence a mother’s overall health and well-being. They [...] Read more.
Being pregnant and giving birth are big life stages that occur for women. The physical and mental effects of pregnancy and childbirth, like those of many other fleeting life experiences, have the significant potential to influence a mother’s overall health and well-being. They have also been known to trigger Postpartum Depression (PPD) in many cases. PPD can be exhausting for the mother and it may have a negative impact on her capacity to care for herself and her kid if it is not treated. For this reason, in this study, initially, physiological questionnaire Edinburgh Postnatal Depression Scale (EPDS) data were collected from delivered mothers for one week, the score was evaluated by medical experts, and participants with PDD symptoms were identified. As a part of multistage progress, further, follow-up was carried out by collecting the Patient Health Questionnaire-9 (PHQ-9), Postpartum Depression Screening Scale (PDSS) questionnaires for the above-predicted participants until six weeks. As the second step, correlated risk factors with PPD symptoms were identified using statistical analysis. Finally, data were analyzed and used to train and test machine learning algorithms in order to predict postpartum depression from one to six weeks. The extremely Randomized Trees (XRT) algorithm with (Background Information + PHQ-9 + PDSS) data offers the most accurate and efficient prediction. Pregnant women with these features could be identified and treated properly. Moreover, it reduces prolonged complications and remains cost-effective in future clinical models. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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9 pages, 1397 KiB  
Article
Detecting Depression Using Single-Channel EEG and Graph Methods
by Guohun Zhu, Tong Qiu, Yi Ding, Shang Gao, Nan Zhao, Feng Liu, Xujuan Zhou and Raj Gururajan
Mathematics 2022, 10(22), 4177; https://doi.org/10.3390/math10224177 - 08 Nov 2022
Cited by 4 | Viewed by 1850
Abstract
Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals. Methods: Four network features—graph entropy, mean degree, degree two, and degree three—were extracted from the 19-channel EEG signals [...] Read more.
Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals. Methods: Four network features—graph entropy, mean degree, degree two, and degree three—were extracted from the 19-channel EEG signals of 64 subjects (26 females and 38 males), and then these features were forwarded to a support vector machine to conduct depression classification based on the eyes-open and eyes-closed statuses, respectively. Results: Statistical analysis showed that graph features with degree of two and three, the graph entropy of MDD was significantly lower than that for H (p < 0.0001). Additionally, the accuracy of detecting MDD using single-channel T4 EEG with leave-one-out cross-validation from H was 89.2% and 92.0% for the eyes-open and eyes-closed statuses, respectively. Conclusion: This study shows that the graph features of a short-term EEG can help assess and evaluate MDD. Thus, single-channel EEG signals can be used to detect depression in subjects. Significance: Graph feature analysis discovered that MDD is more related to the temporal lobe than the frontal lobe. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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15 pages, 4139 KiB  
Article
Deep 3D Volumetric Model Genesis for Efficient Screening of Lung Infection Using Chest CT Scans
by Muhammad Owais, Haseeb Sultan, Na Rae Baek, Young Won Lee, Muhammad Usman, Dat Tien Nguyen, Ganbayar Batchuluun and Kang Ryoung Park
Mathematics 2022, 10(21), 4160; https://doi.org/10.3390/math10214160 - 07 Nov 2022
Cited by 2 | Viewed by 1407
Abstract
In the present outbreak of COVID-19, radiographic imaging modalities such as computed tomography (CT) scanners are commonly used for visual assessment of COVID-19 infection. However, personal assessment of CT images is a time-taking process and demands expert radiologists. Recent advancement in artificial intelligence [...] Read more.
In the present outbreak of COVID-19, radiographic imaging modalities such as computed tomography (CT) scanners are commonly used for visual assessment of COVID-19 infection. However, personal assessment of CT images is a time-taking process and demands expert radiologists. Recent advancement in artificial intelligence field has achieved remarkable performance of computer-aided diagnosis (CAD) methods. Therefore, various deep learning-driven CAD solutions have been proposed for the automatic diagnosis of COVID-19 infection. However, most of them consider limited number of data samples to develop and validate their methods. In addition, various existing methods employ image-based models considering only spatial information in making a diagnostic decision in case of 3D volumetric data. To address these limitations, we propose a dilated shuffle sequential network (DSS-Net) that considers both spatial and 3D structural features in case of volumetric CT data and makes an effective diagnostic decision. To calculate the performance of the proposed DSS-Net, we combined three publicly accessible datasets that include large number of positive and negative data samples. Finally, our DSS-Net exhibits the average performance of 96.58%, 96.53%, 97.07%, 96.01%, and 98.54% in terms of accuracy, F1-score, average precision, average recall, and area under the curve, respectively, and outperforms various state-of-the-art methods. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)
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