An Improved Coronary Heart Disease Predictive System Using Random Forest
Abdulraheem Abdul, Rafiu M. Isiaka, Ronke S. Babatunde, Jumoke F. Ajao
Asian Journal of Research in Computer Science · pp. 17–27 · Published 11 Aug 2021
10.9734/ajrcos/2021/v11i130253Abstract
Aims: This work aim is to develop an enhanced predictive system for Coronary Heart Disease (CHD). Study Design: Synthetic Minority Oversampling Technique and Random Forest. Methodology: The Framingham heart disease dataset was used, which was collected from a study in Framingham, Massachusetts, the data was cleaned, normalized, rebalanced. Classifiers such as random forest, artificial neural network, naïve bayes, logistic regression, k-nearest neighbor and support vector machine were used for classification. Results: Random Forest outperformed other classifiers with an accuracy of 98%, a sensitivity of 99% and a precision of 95.8%. Feature selection was employed for better classification, but no significant improvement was recorded on the performance of the classifier with feature selection. Train test split also performed better that cross validation. Conclusion: Random Forest is recommended for research in Coronary Heart Disease prediction domain.
Cited by 2
Kaushalya Dissanyake, M. Johar · Indonesian Journal of Electrical Engineering and Computer Science · 2023
Suparyati Suparyati, Emma Utami, Alva Hendi Muhammad · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · 2022
Related research
- Detecting Dental Caries through Captured Images Using the Machine Learning Technology Teachable Machine — shares topic coverage
- Prediction of Radiotherapy Dose Distribution for Glioblastoma Using Convolutional Neural Network Model — shares topic coverage
- A Systematic Literature Review of Machine Learning Methods in Healthcare — shares topic coverage
- Diagnostic Accuracy of Artificial Intelligence for Breast Cancer Detection: A Systematic Review — shares topic coverage
- Artificial Intelligence in the Analysis of the Fetal Genome in Utero: A Critical Review of Current Paradigms, Clinical Utility and Future Horizons — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
2
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.