Citation

Yu, Soo Jun and Dollmat, Khairi Shazwan (2024) Comparison of heart disease prediction between basics and a Hybrid machine learning (ML) technique. In: 3rd International Conference on Computer, Information Technology, and Intelligent Computing (CITIC2023), 26–28 July 2023, Virtual Conference.

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Abstract

Heart disease is one of the main causes of death worldwide. Machine learning has been discovered tobe useful in creating predictions from massive amounts of data. We’ve also seen machine learning techniques used in recentadvances in a variety of fields such as medical, finance and even retail. In this research, we used a few traditional ML techniques which is K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, and a Hybrid ML technique that combines Random Forest, SVM and K-NN. We achieved a great performance level with 63.33% accuracy rate of using the hybrid ML model in predicting heart disease. Before applying machine learning techniques, we used feature selection including BORUTA and RFE to identify the Top 10 variables from the dataset to compare with non-using feature selection to build an effective predictive model. Other thanthat, several performance metrics are used to evaluate the results.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Machine learning, Support vector machine, Decision theory, Diseases and conditions, Regression analysis
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Jul 2024 04:20
Last Modified:	31 Jul 2024 04:20
URII:	http://shdl.mmu.edu.my/id/eprint/12684

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