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Diagnostics
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Classification of Diseases Using Machine Learning Algorithms
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Classification of Diseases Using Machine Learning Algorithms

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 12141

Special Issue Editor

Dr. Derya Avcı

E-Mail Website
Guest Editor
Department of Software Engineering, Technology Faculty, Firat University, Elazig, Turkey
Interests: artificial intelligence; images processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical diagnosis is the process of determining the cause of a disease or injury, and it is an essential part of modern healthcare. The ability to accurately diagnose a patient's condition is crucial for the effective treatment and management of disease. A diagnosis of a disease is generally made through the use of physical examination, laboratory tests, imaging tests, or other diagnostic methods. Classification of diseases using machine learning algorithms is an active area of research in the field of medical informatics. With the increasing amount of medical data being generated, machine learning algorithms have the potential to assist physicians and researchers in identifying patterns and making more accurate diagnoses. In this Special Issue, we aim to publish a collection of studies on machine learning algorithms in the classification of diseases using physiological signals and medical images. The medical images of various organs of different X-ray modalities, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), etc. can be employed for disease detection. We hope to publish many original machine and deep learning papers applied to medicine to improve the quality of decision making. In this Special Issue, we focus on the classification of diseases using machine learning algorithms. We welcome high-quality original foundation, research, reviews, and case reports.

Dr. Derya Avcı
Guest Editor

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. Diagnostics 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
  • classification of diseases
  • machine learning
  • medical image processing
  • deep learning
  • neural network

Published Papers (7 papers)

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Research

Jump to: Review, Other

25 pages, 4580 KiB  
Article
Enhancing Diagnostic Decision-Making: Ensemble Learning Techniques for Reliable Stress Level Classification
by Raghav V. Anand, Abdul Quadir Md, Shabana Urooj, Senthilkumar Mohan, Mohamad A. Alawad and Adittya C.
Diagnostics 2023, 13(22), 3455; https://doi.org/10.3390/diagnostics13223455 - 16 Nov 2023
Cited by 2 | Viewed by 1009
Abstract
An intense level of academic pressure causes students to experience stress, and if this stress is not addressed, it can cause adverse mental and physical effects. Since the pandemic situation, students have received more assignments and other tasks due to the shift of [...] Read more.
An intense level of academic pressure causes students to experience stress, and if this stress is not addressed, it can cause adverse mental and physical effects. Since the pandemic situation, students have received more assignments and other tasks due to the shift of classes to an online mode. Students may not realize that they are stressed, but it may be evident from other factors, including sleep deprivation and changes in eating habits. In this context, this paper presents a novel ensemble learning approach that proposes an architecture for stress level classification. It analyzes certain factors such as the sleep hours, productive time periods, screen time, weekly assignments and their submission statuses, and the studying methodology that contribute to stress among the students by collecting a survey from the student community. The survey data are preprocessed to categorize stress levels into three categories: highly stressed, manageable stress, and no stress. For the analysis of the minority class, oversampling methodology is used to remove the imbalance in the dataset, and decision tree, random forest classifier, AdaBoost, gradient boost, and ensemble learning algorithms with various combinations are implemented. To assess the model’s performance, different metrics were used, such as the confusion matrix, accuracy, precision, recall, and F1 score. The results showed that the efficient ensemble learning academic stress classifier gave an accuracy of 93.48% and an F1 score of 93.14%. Fivefold cross-validation was also performed, and an accuracy of 93.45% was achieved. The receiver operating characteristic curve (ROC) value gave an accuracy of 98% for the no-stress category, while providing a 91% true positive rate for manageable and high-stress classes. The proposed ensemble learning with fivefold cross-validation outperformed various state-of-the-art algorithms to predict the stress level accurately. By using these results, students can identify areas for improvement, thereby reducing their stress levels and altering their academic lifestyles, thereby making our stress prediction approach more effective. Full article
(This article belongs to the Special Issue Classification of Diseases Using Machine Learning Algorithms)
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33 pages, 795 KiB  
Article
Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis
by Muniba Saleem, Waqar Aslam, Muhammad Ikram Ullah Lali, Hafiz Tayyab Rauf and Emad Abouel Nasr
Diagnostics 2023, 13(22), 3441; https://doi.org/10.3390/diagnostics13223441 - 14 Nov 2023
Viewed by 1505
Abstract
Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, [...] Read more.
Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score. Full article
(This article belongs to the Special Issue Classification of Diseases Using Machine Learning Algorithms)
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26 pages, 13273 KiB  
Article
Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants
by Ravi Shekhar Tiwari, Lakshmi Dandabani, Tapan Kumar Das, Surbhi Bhatia Khan, Shakila Basheer and Mohammed S. Alqahtani
Diagnostics 2023, 13(22), 3419; https://doi.org/10.3390/diagnostics13223419 - 09 Nov 2023
Viewed by 818
Abstract
The mortality rates of patients contracting the Omicron and Delta variants of COVID-19 are very high, and COVID-19 is the worst variant of COVID. Hence, our objective is to detect COVID-19 Omicron and Delta variants from lung CT-scan images. We designed a unique [...] Read more.
The mortality rates of patients contracting the Omicron and Delta variants of COVID-19 are very high, and COVID-19 is the worst variant of COVID. Hence, our objective is to detect COVID-19 Omicron and Delta variants from lung CT-scan images. We designed a unique ensemble model that combines the CNN architecture of a deep neural network—Capsule Network (CapsNet)—and pre-trained architectures, i.e., VGG-16, DenseNet-121, and Inception-v3, to produce a reliable and robust model for diagnosing Omicron and Delta variant data. Despite the solo model’s remarkable accuracy, it can often be difficult to accept its results. The ensemble model, on the other hand, operates according to the scientific tenet of combining the majority votes of various models. The adoption of the transfer learning model in our work is to benefit from previously learned parameters and lower data-hunger architecture. Likewise, CapsNet performs consistently regardless of positional changes, size changes, and changes in the orientation of the input image. The proposed ensemble model produced an accuracy of 99.93%, an AUC of 0.999 and a precision of 99.9%. Finally, the framework is deployed in a local cloud web application so that the diagnosis of these particular variants can be accomplished remotely. Full article
(This article belongs to the Special Issue Classification of Diseases Using Machine Learning Algorithms)
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25 pages, 6920 KiB  
Article
High-Resolution Network with Dynamic Convolution and Coordinate Attention for Classification of Chest X-ray Images
by Qiang Li, Mingyu Chen, Jingjing Geng, Mohammed Jajere Adamu and Xin Guan
Diagnostics 2023, 13(13), 2165; https://doi.org/10.3390/diagnostics13132165 - 25 Jun 2023
Cited by 2 | Viewed by 1296
Abstract
The development of automatic chest X-ray (CXR) disease classification algorithms is significant for diagnosing thoracic diseases. Owing to the characteristics of lesions in CXR images, including high similarity in appearance of the disease, varied sizes, and different occurrence locations, most existing convolutional neural [...] Read more.
The development of automatic chest X-ray (CXR) disease classification algorithms is significant for diagnosing thoracic diseases. Owing to the characteristics of lesions in CXR images, including high similarity in appearance of the disease, varied sizes, and different occurrence locations, most existing convolutional neural network-based methods have insufficient feature extraction for thoracic lesions and struggle to adapt to changes in lesion size and location. To address these issues, this study proposes a high-resolution classification network with dynamic convolution and coordinate attention (HRCC-Net). In the method, this study suggests a parallel multi-resolution network in which a high-resolution branch acquires essential detailed features of the lesion and multi-resolution feature swapping and fusion to obtain multiple receptive fields to extract complicated disease features adequately. Furthermore, this study proposes dynamic convolution to enhance the network’s ability to represent multi-scale information to accommodate lesions of diverse scales. In addition, this study introduces a coordinate attention mechanism, which enables automatic focus on pathologically relevant regions and capturing the variations in lesion location. The proposed method is evaluated on ChestX-ray14 and CheXpert datasets. The average AUC (area under ROC curve) values reach 0.845 and 0.913, respectively, indicating this method’s advantages compared with the currently available methods. Meanwhile, with its specificity and sensitivity to measure the performance of medical diagnostic systems, the network can improve diagnostic efficiency while reducing the rate of misdiagnosis. The proposed algorithm has great potential for thoracic disease diagnosis and treatment. Full article
(This article belongs to the Special Issue Classification of Diseases Using Machine Learning Algorithms)
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17 pages, 2673 KiB  
Article
A Novel Deep Transfer Learning-Based Approach for Automated Pes Planus Diagnosis Using X-ray Image
by Yeliz Gül, Süleyman Yaman, Derya Avcı, Atilla Hikmet Çilengir, Mehtap Balaban and Hasan Güler
Diagnostics 2023, 13(9), 1662; https://doi.org/10.3390/diagnostics13091662 - 08 May 2023
Viewed by 1905
Abstract
Pes planus, colloquially known as flatfoot, is a deformity defined as the collapse, flattening or loss of the medial longitudinal arch of the foot. The first standard radiographic examination for diagnosing pes planus involves lateral and dorsoplantar weight-bearing radiographs. Recently, many artificial intelligence-based [...] Read more.
Pes planus, colloquially known as flatfoot, is a deformity defined as the collapse, flattening or loss of the medial longitudinal arch of the foot. The first standard radiographic examination for diagnosing pes planus involves lateral and dorsoplantar weight-bearing radiographs. Recently, many artificial intelligence-based computer-aided diagnosis (CAD) systems and models have been developed for the detection of various diseases from radiological images. However, to the best of our knowledge, no model and system has been proposed in the literature for automated pes planus diagnosis using X-ray images. This study presents a novel deep learning-based model for automated pes planus diagnosis using X-ray images, a first in the literature. To perform this study, a new pes planus dataset consisting of weight-bearing X-ray images was collected and labeled by specialist radiologists. In the preprocessing stage, the number of X-ray images was augmented and then divided into 4 and 16 patches, respectively in a pyramidal fashion. Thus, a total of 21 images are obtained for each image, including 20 patches and one original image. These 21 images were then fed to the pre-trained MobileNetV2 and 21,000 features were extracted from the Logits layer. Among the extracted deep features, the most important 1312 features were selected using the proposed iterative ReliefF algorithm, and then classified with support vector machine (SVM). The proposed deep learning-based framework achieved 95.14% accuracy using 10-fold cross validation. The results demonstrate that our transfer learning-based model can be used as an auxiliary tool for diagnosing pes planus in clinical practice. Full article
(This article belongs to the Special Issue Classification of Diseases Using Machine Learning Algorithms)
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Review

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24 pages, 5289 KiB  
Review
Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview
by Alyaa Hamel Sfayyih, Ahmad H. Sabry, Shymaa Mohammed Jameel, Nasri Sulaiman, Safanah Mudheher Raafat, Amjad J. Humaidi and Yasir Mahmood Al Kubaiaisi
Diagnostics 2023, 13(10), 1748; https://doi.org/10.3390/diagnostics13101748 - 16 May 2023
Cited by 5 | Viewed by 2339
Abstract
Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient’s respiratory role. Modern technological progress [...] Read more.
Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient’s respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations. Full article
(This article belongs to the Special Issue Classification of Diseases Using Machine Learning Algorithms)
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Other

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16 pages, 1484 KiB  
Systematic Review
Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms
by Eba’a Dasan Barghouthi, Amani Yousef Owda, Mohammad Asia and Majdi Owda
Diagnostics 2023, 13(17), 2739; https://doi.org/10.3390/diagnostics13172739 - 23 Aug 2023
Cited by 2 | Viewed by 2564
Abstract
Pressure injuries are increasing worldwide, and there has been no significant improvement in preventing them. This study is aimed at reviewing and evaluating the studies related to the prediction model to identify the risks of pressure injuries in adult hospitalized patients using machine [...] Read more.
Pressure injuries are increasing worldwide, and there has been no significant improvement in preventing them. This study is aimed at reviewing and evaluating the studies related to the prediction model to identify the risks of pressure injuries in adult hospitalized patients using machine learning algorithms. In addition, it provides evidence that the prediction models identified the risks of pressure injuries earlier. The systematic review has been utilized to review the articles that discussed constructing a prediction model of pressure injuries using machine learning in hospitalized adult patients. The search was conducted in the databases Cumulative Index to Nursing and Allied Health Literature (CINAHIL), PubMed, Science Direct, the Institute of Electrical and Electronics Engineers (IEEE), Cochrane, and Google Scholar. The inclusion criteria included studies constructing a prediction model for adult hospitalized patients. Twenty-seven articles were included in the study. The defects in the current method of identifying risks of pressure injury led health scientists and nursing leaders to look for a new methodology that helps identify all risk factors and predict pressure injury earlier, before the skin changes or harms the patients. The paper critically analyzes the current prediction models and guides future directions and motivations. Full article
(This article belongs to the Special Issue Classification of Diseases Using Machine Learning Algorithms)
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