R, Swetha and Helen, K.Joy and Yassin, Warusia and R, Sridevi and Drakshayini, M.N and Fabiola, Hazel Pohrmen (2024) Predicting Heart Disease with Machine Learning: A Comparative Study. In: Proceedings of 5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024.
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Abstract
Heart disease is considered as the leading causes of death worldwide. Detection in early stage helps to prevent the disease. Nearly 17 million heart disease deaths occur every year. If heart disease is detected early, it could save many lives. Using machine learning algorithms to detect heart disease early will benefit many people. The present study explains the utilization of machine learning algorithms and artificial intelligence in the detection of cardiac illness. Data science plays a crucial role in healthcare by facilitating the analysis and processing of large volumes of data, therefore enabling the application of Artificial Intelligence (AI) and Machine Learning (ML). This study aims to identify the optimal model for predicting heart disease by employing different classification methods of machine learning, including Logistic Regression (LR) and Support Vector Machine. keywords Artificial intelligence, machine learning, prediction of heart disease, selection of features, decision trees, logistic regression, and accuracy.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Divisions: | Faculty of Information and Communication Technology |
| Depositing User: | NURHASHIRAH BORHAN |
| Date Deposited: | 31 Jul 2026 08:04 |
| Last Modified: | 31 Jul 2026 08:09 |
| URI: | http://eprints.utem.edu.my/id/eprint/30242 |
| Statistic Details: | View Download Statistic |
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