Yassin, Warusia and R, Swetha and K.Joy, Helen and R, Sridevi and M N, Drakshayini and Pohrmen, Fabiola Hazel (2024) Predicting heart disease with machine learning: A comparative study. In: First International Conference, ICCET 2023, 26 May 2023 through 27 May 2023, Lahore, Pakistan.
![[img]](http://eprints.utem.edu.my/style/images/fileicons/text.png) |
Text
Predicting Heart Disease with Machine Learning_ A Comparative Study.pdf Restricted to Registered users only Download (736kB) |
Abstract
Heart disease is a primary health concern worldwide and is responsible for many deaths every year. Early detection and timely management of risk factors can improve outcomes and reduce the burden of this disease. In this study, we explore using machine learning algorithms to predict the risk of heart disease using a dataset containing 14 medical characteristics. We compare the accuracy of six different classifiers, including Naive Bayes, Nearest Neighbors, Random Forest, Gaussian NB, Multinomial NB, and Decision Tree algorithms. Our results show that the Random Forest classifier performed the best with an accuracy of 89%. The use of machine learning in heart disease prediction can help healthcare professionals to make informed decisions and provide personalized care to patients. Developing accurate prediction models can improve health, reduce healthcare costs, and save lives.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Blockchain, Data science, Machine learning, Internet of thing Software defined networking, Deep learning , Cloud computing, Digital forensics, Distributed database |
| Divisions: | Faculty Of Mechanical Technology And Engineering |
| Depositing User: | NUR FARISAH JAFRIN |
| Date Deposited: | 08 Jul 2026 05:25 |
| Last Modified: | 08 Jul 2026 05:25 |
| URI: | http://eprints.utem.edu.my/id/eprint/29781 |
| Statistic Details: | View Download Statistic |
Actions (login required)
 |
View Item |