Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital
Efforts have been made to address the adverse impact of heart disease on society by improving its treatment and diagnosis. This study uses the Jordan University Hospital (JUH) Heart Dataset to develop and evaluate machine-learning models for predicting heart disease. The primary objective of this st...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/5080332 |
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| author | Mohammad Alshraideh Najwan Alshraideh Abedalrahman Alshraideh Yara Alkayed Yasmin Al Trabsheh Bahaaldeen Alshraideh |
| author_facet | Mohammad Alshraideh Najwan Alshraideh Abedalrahman Alshraideh Yara Alkayed Yasmin Al Trabsheh Bahaaldeen Alshraideh |
| author_sort | Mohammad Alshraideh |
| collection | DOAJ |
| description | Efforts have been made to address the adverse impact of heart disease on society by improving its treatment and diagnosis. This study uses the Jordan University Hospital (JUH) Heart Dataset to develop and evaluate machine-learning models for predicting heart disease. The primary objective of this study is to enhance prediction accuracy by utilizing a comprehensive approach that includes data preprocessing, feature selection, and model development. Various artificial intelligence techniques, namely, random forest, SVM, decision tree, naive Bayes, and K-nearest neighbours (KNN) were explored with particle swarm optimization (PSO) for feature selection. These results have substantial implications for early disease detection, diagnosis, and tailored treatment, potentially aiding medical professionals in making well-informed decisions and improving patient outcomes. The PSO is used to select the most compelling features out of 58 features. Experiments on a dataset comprising 486 heart disease patients at JUH yielded a commendable classification accuracy of 94.3% using our proposed system, aligning with state-of-the-art performance. Notably, our research utilized a distinct dataset provided by the corresponding author, while alternative algorithms in our study achieved accuracies ranging from 85% to 90%. These results emphasize the superior accuracy of our proposed system compared to other algorithms considered, particularly highlighting the SVM classifier with PSO as the most accurate, contributing significantly to improving heart disease diagnosis in regions like Jordan, where cardiovascular diseases are a leading cause of mortality. |
| format | Article |
| id | doaj-art-0d6ccc0495f041919e1473ce8c77f953 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-0d6ccc0495f041919e1473ce8c77f9532025-02-03T01:29:42ZengWileyApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/5080332Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University HospitalMohammad Alshraideh0Najwan Alshraideh1Abedalrahman Alshraideh2Yara Alkayed3Yasmin Al Trabsheh4Bahaaldeen Alshraideh5Artificial Intelligence DepartmentMedicine SchoolInternal MedicineInternal Medicine DepartmentMedicine SchoolDivision of UrologyEfforts have been made to address the adverse impact of heart disease on society by improving its treatment and diagnosis. This study uses the Jordan University Hospital (JUH) Heart Dataset to develop and evaluate machine-learning models for predicting heart disease. The primary objective of this study is to enhance prediction accuracy by utilizing a comprehensive approach that includes data preprocessing, feature selection, and model development. Various artificial intelligence techniques, namely, random forest, SVM, decision tree, naive Bayes, and K-nearest neighbours (KNN) were explored with particle swarm optimization (PSO) for feature selection. These results have substantial implications for early disease detection, diagnosis, and tailored treatment, potentially aiding medical professionals in making well-informed decisions and improving patient outcomes. The PSO is used to select the most compelling features out of 58 features. Experiments on a dataset comprising 486 heart disease patients at JUH yielded a commendable classification accuracy of 94.3% using our proposed system, aligning with state-of-the-art performance. Notably, our research utilized a distinct dataset provided by the corresponding author, while alternative algorithms in our study achieved accuracies ranging from 85% to 90%. These results emphasize the superior accuracy of our proposed system compared to other algorithms considered, particularly highlighting the SVM classifier with PSO as the most accurate, contributing significantly to improving heart disease diagnosis in regions like Jordan, where cardiovascular diseases are a leading cause of mortality.http://dx.doi.org/10.1155/2024/5080332 |
| spellingShingle | Mohammad Alshraideh Najwan Alshraideh Abedalrahman Alshraideh Yara Alkayed Yasmin Al Trabsheh Bahaaldeen Alshraideh Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital Applied Computational Intelligence and Soft Computing |
| title | Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital |
| title_full | Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital |
| title_fullStr | Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital |
| title_full_unstemmed | Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital |
| title_short | Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital |
| title_sort | enhancing heart attack prediction with machine learning a study at jordan university hospital |
| url | http://dx.doi.org/10.1155/2024/5080332 |
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