Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Healthcare, Journal Year: 2022, Volume and Issue: 10(3), P. 541 - 541

Published: March 15, 2022

Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms patient population presents massive challenges in developing early diagnosis tool effective treatment. Machine learning (ML), an area artificial intelligence (AI), enables researchers, physicians, patients solve some these issues. Based on relevant research, this review explains how machine (ML) being used help identification numerous diseases. Initially, bibliometric analysis publication carried out using data from Scopus Web Science (WOS) databases. study 1216 publications was undertaken determine most prolific authors, nations, organizations, cited articles. then summarizes recent trends approaches machine-learning-based (MLBDD), considering following factors: algorithm, types, type, application, evaluation metrics. Finally, paper, we highlight key results provides insight into future opportunities MLBDD area.

Language: Английский

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Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

Processes, Journal Year: 2023, Volume and Issue: 11(4), P. 1210 - 1210

Published: April 14, 2023

In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and AdaBoost classifier) are utilized, with datasets from Cleveland IEEE Dataport. Optimizing model accuracy, GridsearchCV, five-fold cross-validation employed. dataset, regression surpassed others 90.16% while excelled in Dataport achieving 90% accuracy. A soft voting ensemble classifier combining all six further enhanced resulting 93.44% for dataset 95% dataset. performance classifiers on both datasets. study’s novelty lies use GridSearchCV hyperparameter optimization, determining best parameters model, assessing negative log loss metrics. also examined each fold to evaluate model’s benchmark The approach improved accuracies and, when compared existing studies, this method notably exceeded their results.

Language: Английский

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Healthcare predictive analytics using machine learning and deep learning techniques: a survey

Journal of Electrical Systems and Information Technology, Journal Year: 2023, Volume and Issue: 10(1)

Published: Aug. 29, 2023

Abstract Healthcare prediction has been a significant factor in saving lives recent years. In the domain of health care, there is rapid development intelligent systems for analyzing complicated data relationships and transforming them into real information use process. Consequently, artificial intelligence rapidly healthcare industry, thus comes role depending on machine learning deep creation steps that diagnose predict diseases, whether from clinical or based images, provide tremendous support by simulating human perception can even diseases are difficult to detect intelligence. Predictive analytics critical imperative industry. It significantly affect accuracy disease prediction, which may lead patients' case accurate timely prediction; contrary, an incorrect it endanger lives. Therefore, must be accurately predicted estimated. Hence, reliable efficient methods predictive analysis essential. this paper aims present comprehensive survey existing approaches utilized identify inherent obstacles applying these domain.

Language: Английский

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A Review of Machine Learning Algorithms for Biomedical Applications

Annals of Biomedical Engineering, Journal Year: 2024, Volume and Issue: 52(5), P. 1159 - 1183

Published: Feb. 21, 2024

Language: Английский

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Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Computational and Mathematical Methods in Medicine, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 14

Published: May 2, 2022

Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option reducing and understanding heart symptoms of disease. The chi-square statistical test performed to select specific attributes from the Cleveland (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, random forest algorithm have been employed developing risk model obtained accuracy as 80.32%, 78.68%, 77.04%, 73.77%, 88.5%, respectively. data visualization has generated illustrate relationship between features. According findings experiments, achieves 88.5% during validation 303 instances with 13 selected features HD

Language: Английский

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AI-Based Approaches for the Diagnosis of Mpox: Challenges and Future Prospects

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3585 - 3617

Published: March 26, 2024

Language: Английский

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PREDICTING THE PREVALENCE OF CARDIOVASCULAR DISEASES USING MACHINE LEARNING ALGORITHMS

Intelligence-Based Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 100199 - 100199

Published: Jan. 1, 2025

Language: Английский

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Exploring Important Factors in Predicting Heart Disease Based on Ensemble- Extra Feature Selection Approach

Baghdad Science Journal, Journal Year: 2024, Volume and Issue: 21(2(SI)), P. 0812 - 0812

Published: Feb. 25, 2024

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with rise big data medical datasets, it has become increasingly challenging practitioners accurately predict heart due abundance unrelated redundant features hinder computational complexity accuracy. As such, this study aims identify most discriminative within high-dimensional while minimizing improving accuracy through an Extra Tree feature selection based technique. The work assesses efficacy several classification algorithms on four reputable using both full set reduced subset selected proposed method. results show technique achieves outstanding accuracy, precision, recall, impressive 97% when used classifier algorithm. research reveals promising potential method by focusing informative simultaneously decreasing burden.

Language: Английский

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Ensemble-based Heart Disease Diagnosis (EHDD) Using Feature Selection and PCA Extraction Methods

The Open Bioinformatics Journal, Journal Year: 2025, Volume and Issue: 18(1)

Published: Feb. 6, 2025

Introduction Heart disease is a growing health crisis in India, with mortality rates on the rise alongside population. Numerous studies have been undertaken to understand, predict, and prevent this critical illness. The dimensionality of dataset, other hand, reduces prediction's accuracy. Methods We propose an Ensemble-based Disease Diagnosis (EHDD) model which dimension lowered through filter-based feature selection. experimental conducted using UCI Cleveland dataset cardiac disease. precision achieved three key steps. scatter matrix utilized divide distinct class points first phase, highest eigenvalue eigenvectors are picked for new decreased dataset. extraction carried out second stage utilizing statistical approach based mean, covariance, standard deviation. Results classification component uses training test datasets smaller sample space. last samples into two groups: healthy subjects diseased subjects. Since basic binary classifier will not yield best results, ensemble strategy SVM. Conclusion Random Forest chosen create accurate predictions. When compared existing models, suggested EHDD outperforms them by 98%.

Language: Английский

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Enhanced Diagnostic Precision for Cardiovascular Diseases through the Synergistic Application of GDE_Lasso Feature Selection and Random Forest Classification Techniques

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: March 21, 2025

Cardiovascular diseases (CVD) pose a significant global health challenge, contributing substantially to mortality rates worldwide. Early detection and diagnosis of CVD are critical, machine learning techniques offer promising avenues for analyzing risk factors implementing preventive measures. Feature selection methods can also help reduce diagnostic costs. Hence, in this work, Gaussian-based differential entropy information gain with the Lasso (GDE_Lasso) feature model is proposed. The goal optimize diagnostics by streamlining processes, minimizing tests, enabling targeted interventions. proposed evaluated on Cleveland Datasets 1 2, respectively. This work compares performance Logistic Regression, Naïve Bayes, SVM, KNN, Decision Tree, XG Boost, Random Forest considered datasets applying Z-score method. It was found that performs well among classifiers. Therefore, study evaluates without GDE_Lasso algorithm.

Language: Английский

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