Research

17 pages, 928 KiB

Open AccessArticle

Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization

by Daniyal Asif, Mairaj Bibi, Muhammad Shoaib Arif and Aiman Mukheimer

Algorithms 2023, 16(6), 308; https://doi.org/10.3390/a16060308 - 20 Jun 2023

Cited by 14 | Viewed by 5654

Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early and accurate heart disease prediction is crucial for effectively preventing and managing the condition. However, this remains a challenging task to achieve. This study proposes a machine [...] Read more.

Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early and accurate heart disease prediction is crucial for effectively preventing and managing the condition. However, this remains a challenging task to achieve. This study proposes a machine learning model that leverages various preprocessing steps, hyperparameter optimization techniques, and ensemble learning algorithms to predict heart disease. To evaluate the performance of our model, we merged three datasets from Kaggle that have similar features, creating a comprehensive dataset for analysis. By employing the extra tree classifier, normalizing the data, utilizing grid search cross-validation (CV) for hyperparameter optimization, and splitting the dataset with an 80:20 ratio for training and testing, our proposed approach achieved an impressive accuracy of 98.15%. These findings demonstrated the potential of our model for accurately predicting the presence or absence of heart disease. Such accurate predictions could significantly aid in early prevention, detection, and treatment, ultimately reducing the mortality and morbidity associated with heart disease. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)

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12 pages, 836 KiB

Open AccessArticle

Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department

by Massimiliano Greco, Pier Francesco Caruso, Sofia Spano, Gianluigi Citterio, Antonio Desai, Alberto Molteni, Romina Aceto, Elena Costantini, Antonio Voza and Maurizio Cecconi

Algorithms 2023, 16(2), 76; https://doi.org/10.3390/a16020076 - 01 Feb 2023

Cited by 2 | Viewed by 1936

Abstract

Background: Sepsis is one of the major causes of in-hospital death, and is frequent in patients presenting to the emergency department (ED). Early identification of high-risk septic patients is critical. Machine learning (ML) techniques have been proposed for identification and prognostication of ED [...] Read more.

Background: Sepsis is one of the major causes of in-hospital death, and is frequent in patients presenting to the emergency department (ED). Early identification of high-risk septic patients is critical. Machine learning (ML) techniques have been proposed for identification and prognostication of ED septic patients, but these models often lack pre-hospital data and lack validation against early sepsis identification scores (such as qSOFA) and scores for critically ill patients (SOFA, APACHE II). Methods We conducted an electronic health record (EHR) study to test whether interpretable and scalable ML models predict mortality in septic ED patients and compared their performance with clinical scores. Consecutive adult septic patients admitted to ED over 18 months were included. We built ML models, ranging from a simple-classifier model, to unbalanced and balanced logistic regression, and random forest, and compared their performance to qSOFA, SOFA, and APACHE II scores. Results: We included 425 sepsis patients after screening 38,500 EHR for sepsis criteria. Overall mortality was 15.2% and peaked in patients coming from retirement homes (38%). Random forest, like balanced (0.811) and unbalanced logistic regression (0.863), identified patients at risk of mortality (0.813). All ML models outperformed qSOFA, APACHE II, and SOFA scores. Age, mean arterial pressure, and serum sodium were major mortality predictors. Conclusions: We confirmed that random forest models outperform previous models, including qSOFA, SOFA, and APACHE II, in identifying septic patients at higher mortality risk, while maintaining good interpretability. Machine learning models may gain further adoption in the future with increasing diffusion and granularity of EHR data, yielding the advantage of increased scalability compared to standard statistical techniques. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)

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22 pages, 3259 KiB

Open AccessArticle

Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems

by Roseline Oluwaseun Ogundokun, Rytis Maskeliūnas, Sanjay Misra and Robertas Damasevicius

Algorithms 2022, 15(11), 410; https://doi.org/10.3390/a15110410 - 04 Nov 2022

Cited by 9 | Viewed by 3417

Abstract

Posture detection targets toward providing assessments for the monitoring of the health and welfare of humans have been of great interest to researchers from different disciplines. The use of computer vision systems for posture recognition might result in useful improvements in healthy aging [...] Read more.

Posture detection targets toward providing assessments for the monitoring of the health and welfare of humans have been of great interest to researchers from different disciplines. The use of computer vision systems for posture recognition might result in useful improvements in healthy aging and support for elderly people in their daily activities in the field of health care. Computer vision and pattern recognition communities are particularly interested in fall automated recognition. Human sensing and artificial intelligence have both paid great attention to human posture detection (HPD). The health status of elderly people can be remotely monitored using human posture detection, which can distinguish between positions such as standing, sitting, and walking. The most recent research identified posture using both deep learning (DL) and conventional machine learning (ML) classifiers. However, these techniques do not effectively identify the postures and overfits of the model overfits. Therefore, this study suggested a deep convolutional neural network (DCNN) framework to examine and classify human posture in health monitoring systems. This study proposes a feature selection technique, DCNN, and a machine learning technique to assess the previously mentioned problems. The InceptionV3 DCNN model is hybridized with SVM ML and its performance is compared. Furthermore, the performance of the proposed system is validated with other transfer learning (TL) techniques such as InceptionV3, DenseNet121, and ResNet50. This study uses the least absolute shrinkage and selection operator (LASSO)-based feature selection to enhance the feature vector. The study also used various techniques, such as data augmentation, dropout, and early stop, to overcome the problem of model overfitting. The performance of this DCNN framework is tested using benchmark Silhouettes of human posture and classification accuracy, loss, and AUC value of 95.42%, 0.01, and 99.35% are attained, respectively. Furthermore, the results of the proposed technology offer the most promising solution for indoor monitoring systems. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)

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9 pages, 7442 KiB

Open AccessArticle

Reduction of Video Capsule Endoscopy Reading Times Using Deep Learning with Small Data

by Hunter Morera, Roshan Warman, Azubuogu Anudu, Chukwudumebi Uche, Ivana Radosavljevic, Nikhil Reddy, Ahan Kayastha, Niharika Baviriseaty, Rahul Mhaskar, Andrew A. Borkowski, Patrick Brady, Satish Singh, Gerard Mullin, Jose Lezama, Lawrence O. Hall, Dmitry Goldgof and Gitanjali Vidyarthi

Algorithms 2022, 15(10), 339; https://doi.org/10.3390/a15100339 - 21 Sep 2022

Cited by 2 | Viewed by 2086

Abstract

Video capsule endoscopy (VCE) is an innovation that has revolutionized care within the field of gastroenterology, but the time needed to read the studies generated has often been cited as an area for improvement. With the aid of artificial intelligence, various fields have [...] Read more.

Video capsule endoscopy (VCE) is an innovation that has revolutionized care within the field of gastroenterology, but the time needed to read the studies generated has often been cited as an area for improvement. With the aid of artificial intelligence, various fields have been able to improve the efficiency of their core processes by reducing the burden of irrelevant stimuli on their human elements. In this study, we have created and trained a convolutional neural network (CNN) capable of significantly reducing capsule endoscopy reading times by eliminating normal parts of the video while retaining abnormal ones. Our model, a variation of ResNet50, was able to reduce VCE video length by 47% on average and capture abnormal segments on VCE with 100% accuracy on three VCE videos as confirmed by the reading physician. The ability to successfully pre-process VCE footage as we have demonstrated will greatly increase the practicality of VCE technology without the expense of hundreds of hours of physician annotated videos. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)

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15 pages, 2158 KiB

Open AccessFeature PaperArticle

Early Prediction of Chronic Kidney Disease: A Comprehensive Performance Analysis of Deep Learning Models

by Chaity Mondol, F. M. Javed Mehedi Shamrat, Md. Robiul Hasan, Saidul Alam, Pronab Ghosh, Zarrin Tasnim, Kawsar Ahmed, Francis M. Bui and Sobhy M. Ibrahim

Algorithms 2022, 15(9), 308; https://doi.org/10.3390/a15090308 - 29 Aug 2022

Cited by 9 | Viewed by 3179

Abstract

Chronic kidney disease (CKD) is one of the most life-threatening disorders. To improve survivability, early discovery and good management are encouraged. In this paper, CKD was diagnosed using multiple optimized neural networks against traditional neural networks on the UCI machine learning dataset, to [...] Read more.

Chronic kidney disease (CKD) is one of the most life-threatening disorders. To improve survivability, early discovery and good management are encouraged. In this paper, CKD was diagnosed using multiple optimized neural networks against traditional neural networks on the UCI machine learning dataset, to identify the most efficient model for the task. The study works on the binary classification of CKD from 24 attributes. For classification, optimized CNN (OCNN), ANN (OANN), and LSTM (OLSTM) models were used as well as traditional CNN, ANN, and LSTM models. With various performance matrixes, error measures, loss values, AUC values, and compilation time, the implemented models are compared to identify the most competent model for the classification of CKD. It is observed that, overall, the optimized models have better performance compared to the traditional models. The highest validation accuracy among the tradition models were achieved from CNN with 92.71%, whereas OCNN, OANN, and OLSTM have higher accuracies of 98.75%, 96.25%, and 98.5%, respectively. Additionally, OCNN has the highest AUC score of 0.99 and the lowest compilation time for classification with 0.00447 s, making it the most efficient model for the diagnosis of CKD. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)

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20 pages, 3690 KiB

Open AccessArticle

Artificial Intelligence Algorithms for Treatment of Diabetes

by Mudassir M. Rashid, Mohammad Reza Askari, Canyu Chen, Yueqing Liang, Kai Shu and Ali Cinar

Algorithms 2022, 15(9), 299; https://doi.org/10.3390/a15090299 - 26 Aug 2022

Cited by 9 | Viewed by 3474

Abstract

Artificial intelligence (AI) algorithms can provide actionable insights for clinical decision-making and managing chronic diseases. The treatment and management of complex chronic diseases, such as diabetes, stands to benefit from novel AI algorithms analyzing the frequent real-time streaming data and the occasional medical [...] Read more.

Artificial intelligence (AI) algorithms can provide actionable insights for clinical decision-making and managing chronic diseases. The treatment and management of complex chronic diseases, such as diabetes, stands to benefit from novel AI algorithms analyzing the frequent real-time streaming data and the occasional medical diagnostics and laboratory test results reported in electronic health records (EHR). Novel algorithms are needed to develop trustworthy, responsible, reliable, and robust AI techniques that can handle the imperfect and imbalanced data of EHRs and inconsistencies or discrepancies with free-living self-reported information. The challenges and applications of AI for two problems in the healthcare domain were explored in this work. First, we introduced novel AI algorithms for EHRs designed to be fair and unbiased while accommodating privacy concerns in predicting treatments and outcomes. Then, we studied the innovative approach of using machine learning to improve automated insulin delivery systems through analyzing real-time information from wearable devices and historical data to identify informative trends and patterns in free-living data. Application examples in the treatment of diabetes demonstrate the benefits of AI tools for medical and health informatics. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)

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29 pages, 10473 KiB

Open AccessArticle

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

by Alexander V. Khoperskov and Maxim V. Polyakov

Algorithms 2022, 15(8), 292; https://doi.org/10.3390/a15080292 - 17 Aug 2022

Cited by 5 | Viewed by 2014

Abstract

This work includes a brief overview of the applications of the powerful and easy-to-perform method of microwave radiometry (MWR) for the diagnosis of various diseases. The main goal of this paper is to develop a method for diagnosing breast oncology based on machine [...] Read more.

This work includes a brief overview of the applications of the powerful and easy-to-perform method of microwave radiometry (MWR) for the diagnosis of various diseases. The main goal of this paper is to develop a method for diagnosing breast oncology based on machine learning algorithms using thermometric data, both real medical measurements and simulation results of MWR examinations. The dataset includes distributions of deep and skin temperatures calculated in numerical models of the dynamics of thermal and radiation fields inside biological tissue. The constructed combined dataset allows us to explore the limits of applicability of the MWR method for detecting weak tumors. We use convolutional neural networks and classic machine learning algorithms (k-nearest neighbors, naive Bayes classifier, support vector machine) to classify data. The construction of Kohonen self-organizing maps to explore the structure of our combined dataset demonstrated differences between the temperatures of patients with positive and negative diagnoses. Our analysis shows that the MWR can detect tumors with a radius of up to 0.5 cm if they are at the stage of rapid growth, when the tumor volume doubling occurs in approximately 100 days or less. The use of convolutional neural networks for MWR provides both high sensitivity (

s e n s = 0.86

) and specificity (

s p e c = 0.82

), which is an advantage over other methods for diagnosing breast cancer. A new modified scheme for medical measurements of IR temperature and brightness temperature is proposed for a larger number of points in the breast compared to the classical scheme. This approach can increase the effectiveness and sensitivity of diagnostics by several percent. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)

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17 pages, 1070 KiB

Open AccessArticle

Multifractal Characterization and Modeling of Blood Pressure Signals

by Enrico De Santis, Parisa Naraei, Alessio Martino, Alireza Sadeghian and Antonello Rizzi

Algorithms 2022, 15(8), 259; https://doi.org/10.3390/a15080259 - 26 Jul 2022

Viewed by 1668

Abstract

In this paper, a multi-fractal analysis on a diastolic blood pressure signal is conducted. The signal is measured in a time span of circa one day through the multifractal detrended fluctuation analysis framework. The analysis is performed on asymptotic timescales where complex regulating [...] Read more.

In this paper, a multi-fractal analysis on a diastolic blood pressure signal is conducted. The signal is measured in a time span of circa one day through the multifractal detrended fluctuation analysis framework. The analysis is performed on asymptotic timescales where complex regulating mechanisms play a fundamental role in the blood pressure stability. Given a suitable frequency range and after removing non-stationarities, the blood pressure signal shows interesting scaling properties and a pronounced multifractality imputed to long-range correlations. Finally, a binomial multiplicative model is investigated showing how the analyzed signal can be described by a concise multifractal model with only two parameters. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)

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14 pages, 2547 KiB

Open AccessArticle

Construction of Life-Cycle Simulation Framework of Chronic Diseases and Their Comorbidities Based on Population Cohort

by Peixia Sun, Shengxiong Lao, Dongyang Du, Jiqiang Peng and Xu Yang

Algorithms 2022, 15(5), 167; https://doi.org/10.3390/a15050167 - 16 May 2022

Cited by 1 | Viewed by 1769

Abstract

Life-cycle population follow-up data collection is time-consuming and often takes decades. General cohort data studies collect short-to-medium-term data from populations of different age groups. The purpose of constructing a life-cycle simulation method is to find an efficient and reliable way to achieve the [...] Read more.

Life-cycle population follow-up data collection is time-consuming and often takes decades. General cohort data studies collect short-to-medium-term data from populations of different age groups. The purpose of constructing a life-cycle simulation method is to find an efficient and reliable way to achieve the way to characterize life-cycle disease metastasis from these short-to-medium-term data. In this paper, we have presented our effort at construction of a full lifetime population cohort simulation framework. The design aim is to generate a comprehensive understanding of the disease transition for full lifetime when we only have short-or-medium term population cohort data. We have conducted several groups of experiments to show the effectiveness of our method. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine)

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