Bhuiyan, Mohaiminul Islam and Chan, Hue Wah and Nur Shazwani, Kamarudin and Nur Hafieza, Ismail and Ahmad Fakhri, Ab Nasir (2024) Heart disease prediction using case based reasoning (CBR). Journal of Theoretical and Applied Information Technology, 102 (20). pp. 7275-7186. ISSN 1992-8645 (print); 817-3195 (online). (Published)
|
Pdf (Open Access)
HEART DISEASE PREDICTION USING CASE BASED REASONING (CBR).pdf Download (1MB) | Preview |
Abstract
This study provides an overview of heart disease prediction using an intelligent system. Predicting disease accurately is crucial in the medical field, but traditional methods relying solely on a doctor's experience often lack precision. To address this limitation, intelligent systems are applied as an alternative to traditional approaches. While various intelligent system methods exist, this study focuses on three: Fuzzy Logic, Neural Networks, and Case-Based Reasoning (CBR). A comparison of these techniques in terms of accuracy was conducted, and ultimately, Case-Based Reasoning (CBR) was selected for heart disease prediction. In the prediction phase, the heart disease dataset underwent data pre-processing to clean the data and data splitting to separate it into training and testing sets. The chosen intelligent system was then employed to predict heart disease outcomes based on the processed data. The experiment concluded with Case-Based Reasoning (CBR) achieving a notable accuracy rate of 97.95% in predicting heart disease. The findings also revealed that the probability of heart disease was 57.76% for males and 42.24% for females. Further analysis from related studies suggests that factors such as smoking and alcohol consumption are significant contributors to heart disease, particularly among males.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Case Base Reasoning (CBR; Machine Learning; Heart Disease |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 13 Jan 2025 03:51 |
| Last Modified: | 13 Jan 2025 03:51 |
| URI: | http://umpir.ump.edu.my/id/eprint/40564 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |