Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Nalinpat Bhumpenpein
King Mongkut's University of Technology North Bangkok
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Interpretable machine learning framework for congestive heart failure risk prediction Sakchai Tangprasert; Nalinpat Bhumpenpein; Siranee Nuchitprasitchai; Yuenyong Nilsiam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4345-4355

Abstract

Congestive heart failure (CHF) is a leading cause of morbidity and mortality worldwide and is influenced by multiple clinical risk factors, including age, sex, blood pressure, cholesterol levels, and electrocardiographic characteristics. This study employs publicly available heart disease datasets compiled from populations in Cleveland, Hungary, Switzerland, and Long Beach repositories (2015–2020) and analyzes 11 predictor attributes and one binary outcome using the cross-industry standard process for data mining (CRISP-DM) framework. Four supervised machine learning (ML) models—decision tree, random forest, gradient boosted trees, and naïve Bayes—were developed and evaluated using confusion matrix–based performance metrics, with results showing that the decision tree model offers balanced predictive performance and clear interpretability, making it suitable for heart failure risk prediction. The findings demonstrate the potential of ML approaches to support clinical risk assessment and enhance decision-making in heart failure management.
Co-Authors Sakchai Tangprasert Siranee Nuchitprasitchai Yuenyong Nilsiam