Risk Factor Prediction for Heart Disease Using Decision Trees
Authors: 
Shanthi Kunchi, Vijaya N. Aher, Sharayu Ikhar, Kishor Pathak, Yatin Gandhi, Kirti WanjaleAuthors Info & Claims
Article No.: 110, Pages 1 - 6
Abstract
Global mortality rates are high due to heart disease, it is crucial to early analyzing the risk factors of prediction and diagnosis are crucial. Decision trees are a reliable machine-learning method for this problem. Four decision tree algorithms CART (Classification and Regression Trees), C5.0, M5P (Model Tree with Linear Models in Leaves), and XGBoost are tested for heart disease risk factor prediction. An extensive dataset of heart disease risk factors was used to evaluate decision tree algorithms. To assess model performance rigorously, the study used data preprocessing, feature selection, and stratified cross-validation. The results from the experiments were significant. Despite its simplicity, the CART algorithm predicted heart disease risk factors accurately. The C5.0 algorithm handled missing data well, a common healthcare dataset issue. M5P, which integrates linear models into leaf nodes, improves interpretability without compromising accuracy. XGBoost was the best model, predicting risk factors with 99.32% accuracy. The result shows the algorithm's durability and suitability for complex medical diagnostic tasks. Decision tree algorithms predict heart disease risk factors effectively and clearly. XGBoost was the best decision tree algorithm due to its accuracy and reliability. This technology's precision suggests it could help doctors make quick, accurate diagnoses, potentially saving lives. This study highlights the importance of using machine learning and decision trees to improve heart disease risk assessment and patient care. Further research could examine the integration of XGBoost and other decision tree models into clinical practice to improve heart disease detection and treatment.
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Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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