Comprehensive evaluation and performance analysis of machine learning in heart disease prediction
- Title
- Comprehensive evaluation and performance analysis of machine learning in heart disease prediction
- Creator
- Al-Alshaikh H.A.; Prabu P.; Poonia R.C.; Saudagar A.K.J.; Yadav M.; AlSagri H.S.; AlSanad A.A.
- Description
- Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the models robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the models predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The systems sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes. The Author(s) 2024.
- Source
- Scientific Reports, Vol-14, No. 1
- Date
- 2024-01-01
- Publisher
- Nature Research
- Subject
- Healthcare; Heart disease; Machine learning; Prediction
- Coverage
- Al-Alshaikh H.A., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Prabu P., Department of Computer Science, CHRIST University, Bangalore, 560029, India; Poonia R.C., Department of Computer Science, CHRIST University, Bangalore, 560029, India; Saudagar A.K.J., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Yadav M., Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, India; AlSagri H.S., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; AlSanad A.A., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 20452322; PubMed ID: 38570582
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Al-Alshaikh H.A.; Prabu P.; Poonia R.C.; Saudagar A.K.J.; Yadav M.; AlSagri H.S.; AlSanad A.A., “Comprehensive evaluation and performance analysis of machine learning in heart disease prediction,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/12760.