Heart disease prediction using case based reasoning (CBR)
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 appli...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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JATIT
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40564/ http://umpir.ump.edu.my/id/eprint/40564/1/HEART%20DISEASE%20PREDICTION%20USING%20CASE%20BASED%20REASONING%20%28CBR%29.pdf |
| _version_ | 1848826083964616704 |
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| author | Bhuiyan, Mohaiminul Islam Chan, Hue Wah Nur Shazwani, Kamarudin Nur Hafieza, Ismail Ahmad Fakhri, Ab Nasir |
| author_facet | Bhuiyan, Mohaiminul Islam Chan, Hue Wah Nur Shazwani, Kamarudin Nur Hafieza, Ismail Ahmad Fakhri, Ab Nasir |
| author_sort | Bhuiyan, Mohaiminul Islam |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:39:11Z |
| format | Article |
| id | ump-40564 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:39:11Z |
| publishDate | 2024 |
| publisher | JATIT |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-405642025-04-28T04:54:07Z http://umpir.ump.edu.my/id/eprint/40564/ Heart disease prediction using case based reasoning (CBR) Bhuiyan, Mohaiminul Islam Chan, Hue Wah Nur Shazwani, Kamarudin Nur Hafieza, Ismail Ahmad Fakhri, Ab Nasir QA75 Electronic computers. Computer science RC Internal medicine 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. JATIT 2024 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40564/1/HEART%20DISEASE%20PREDICTION%20USING%20CASE%20BASED%20REASONING%20%28CBR%29.pdf 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) http://www.jatit.org/volumes/Vol102No20/3Vol102No20.pdf https://www.jatit.org/volumes/Vol102No20/3Vol102No20.pdf |
| spellingShingle | QA75 Electronic computers. Computer science RC Internal medicine Bhuiyan, Mohaiminul Islam Chan, Hue Wah Nur Shazwani, Kamarudin Nur Hafieza, Ismail Ahmad Fakhri, Ab Nasir Heart disease prediction using case based reasoning (CBR) |
| title | Heart disease prediction using case based reasoning (CBR) |
| title_full | Heart disease prediction using case based reasoning (CBR) |
| title_fullStr | Heart disease prediction using case based reasoning (CBR) |
| title_full_unstemmed | Heart disease prediction using case based reasoning (CBR) |
| title_short | Heart disease prediction using case based reasoning (CBR) |
| title_sort | heart disease prediction using case based reasoning (cbr) |
| topic | QA75 Electronic computers. Computer science RC Internal medicine |
| url | http://umpir.ump.edu.my/id/eprint/40564/ http://umpir.ump.edu.my/id/eprint/40564/ http://umpir.ump.edu.my/id/eprint/40564/ http://umpir.ump.edu.my/id/eprint/40564/1/HEART%20DISEASE%20PREDICTION%20USING%20CASE%20BASED%20REASONING%20%28CBR%29.pdf |