AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING
Cardiovascular diseases (CD) are the common cause of death worldwide over in developed as well as underdeveloped and developing countries. Early detection and continuous supervision can reduce the mortality rate. Cardiovascular disease diagnosis and accurate diagnosis to enable early treatment. So...
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Institute of Mechanics of Continua and Mathematical Sciences
2025-03-01
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| Series: | Journal of Mechanics of Continua and Mathematical Sciences |
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| author | Muhammad Anas Muhammad Atif Imtiaz Saad Khan Arshad Ali Noor Fatima Naghman Hamayun Khan Sami Albouq |
| author_facet | Muhammad Anas Muhammad Atif Imtiaz Saad Khan Arshad Ali Noor Fatima Naghman Hamayun Khan Sami Albouq |
| author_sort | Muhammad Anas |
| collection | DOAJ |
| description | Cardiovascular diseases (CD) are the common cause of death worldwide
over in developed as well as underdeveloped and developing countries. Early
detection and continuous supervision can reduce the mortality rate. Cardiovascular disease diagnosis and accurate diagnosis to enable early treatment. Some of these techniques do not easily diagnose heart diseases at early stages hence, getting treatment late poses a big risk. The present work attempts to better predict this disease from the chest pain symptom, and classify it by designing an efficient machine learning system based on a dataset with 303 patient data made available to the public domain. The four machine learning algorithms that were used for the analysis include Logistic Regression, Random Forest, Support Vector Machines, and Neural Networks to determine which of them is most appropriate for predicting heart diseases. Original data was preprocessed by handling missing values, normalizing features, and using feature extraction techniques. Splitting the dataset into 80% training and 20% testing, cross-validation was performed to validate outcomes on all four models.
Although the highest accuracy was reached by the model of the Neural Network by 97%, it was revealed to have tendencies of overfitting. The SVM model achieved the highest accuracy of 97%, and was the most stable and interpretable; therefore, it was considered to be the most suitable for clinical use. Base on the study, there is a promise to champion the use of machine learning models for timely diagnosis of heart diseases by medical practitioners to enhance patient success rates and the overworked health facilities’ performance. The next steps will consist in enlarging a database and implementing these models in supporting clinical practice with realtime diagnostic potential. As a result, the doctors can visualize the patient’s real-time sensor data using the application and start live video streaming if instant medication is required. The proposed system is notified at once through GSM technology. |
| format | Article |
| id | doaj-art-e62860a5af6d4875a3745b3d9dec44b6 |
| institution | DOAJ |
| issn | 0973-8975 2454-7190 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Institute of Mechanics of Continua and Mathematical Sciences |
| record_format | Article |
| series | Journal of Mechanics of Continua and Mathematical Sciences |
| spelling | doaj-art-e62860a5af6d4875a3745b3d9dec44b62025-08-20T03:13:08ZengInstitute of Mechanics of Continua and Mathematical SciencesJournal of Mechanics of Continua and Mathematical Sciences0973-89752454-71902025-03-01203547210.26782/jmcms.2025.03.00005AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNINGMuhammad Anas0Muhammad Atif Imtiaz1Saad Khan2Arshad Ali3Noor Fatima Naghman4Hamayun Khan5Sami Albouq6Department of Computer Science & IT Superior University Lahore, 54000, Pakistan.School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia.Department of Computer Science & IT Superior University Lahore, 54000, Pakistan.Department of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, 42351, Saudi Arabia.Department of Computer Science & IT Superior University Lahore, 54000, Pakistan.Department of Computer Science & IT Superior University Lahore, 54000, Pakistan.Department of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, 42351, Saudi Arabia.Cardiovascular diseases (CD) are the common cause of death worldwide over in developed as well as underdeveloped and developing countries. Early detection and continuous supervision can reduce the mortality rate. Cardiovascular disease diagnosis and accurate diagnosis to enable early treatment. Some of these techniques do not easily diagnose heart diseases at early stages hence, getting treatment late poses a big risk. The present work attempts to better predict this disease from the chest pain symptom, and classify it by designing an efficient machine learning system based on a dataset with 303 patient data made available to the public domain. The four machine learning algorithms that were used for the analysis include Logistic Regression, Random Forest, Support Vector Machines, and Neural Networks to determine which of them is most appropriate for predicting heart diseases. Original data was preprocessed by handling missing values, normalizing features, and using feature extraction techniques. Splitting the dataset into 80% training and 20% testing, cross-validation was performed to validate outcomes on all four models. Although the highest accuracy was reached by the model of the Neural Network by 97%, it was revealed to have tendencies of overfitting. The SVM model achieved the highest accuracy of 97%, and was the most stable and interpretable; therefore, it was considered to be the most suitable for clinical use. Base on the study, there is a promise to champion the use of machine learning models for timely diagnosis of heart diseases by medical practitioners to enhance patient success rates and the overworked health facilities’ performance. The next steps will consist in enlarging a database and implementing these models in supporting clinical practice with realtime diagnostic potential. As a result, the doctors can visualize the patient’s real-time sensor data using the application and start live video streaming if instant medication is required. The proposed system is notified at once through GSM technology.https://jmcms.s3.amazonaws.com/wp-content/uploads/2025/03/15105344/jmcms-2501001-An-Advance-Machine-Learning-1.pdfartificial intelligencedata preprocessingmachine learninglogistic regressionneural networkspredictive modeling. random forestsvm. |
| spellingShingle | Muhammad Anas Muhammad Atif Imtiaz Saad Khan Arshad Ali Noor Fatima Naghman Hamayun Khan Sami Albouq AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING Journal of Mechanics of Continua and Mathematical Sciences artificial intelligence data preprocessing machine learning logistic regression neural networks predictive modeling. random forest svm. |
| title | AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING |
| title_full | AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING |
| title_fullStr | AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING |
| title_full_unstemmed | AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING |
| title_short | AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING |
| title_sort | advanced machine learning ml architecture for heart disease detection prediction and classification using machine learning |
| topic | artificial intelligence data preprocessing machine learning logistic regression neural networks predictive modeling. random forest svm. |
| url | https://jmcms.s3.amazonaws.com/wp-content/uploads/2025/03/15105344/jmcms-2501001-An-Advance-Machine-Learning-1.pdf |
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