Research Article
Early Prediction of Coronary Heart Disease Using the Boruta Method
@INPROCEEDINGS{10.1007/978-3-031-35078-8_11, author={Vaibhav Satija and Mohaneesh Raj Pradhan and Princy Randhawa}, title={Early Prediction of Coronary Heart Disease Using the Boruta Method}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I}, proceedings_a={ICISML}, year={2023}, month={7}, keywords={Healthcare Heart Failure Prediction Machine Learning Algorithms Chronic Heart Disease Boruta Synthetic Minority Oversampling Technique}, doi={10.1007/978-3-031-35078-8_11} }- Vaibhav Satija
Mohaneesh Raj Pradhan
Princy Randhawa
Year: 2023
Early Prediction of Coronary Heart Disease Using the Boruta Method
ICISML
Springer
DOI: 10.1007/978-3-031-35078-8_11
Abstract
This paper discusses the application of machine learning in the healthcare sector for the prediction of heart disease. Because technology is a valuable tool in the healthcare industry, we intend to discuss the development of a machine learning model that measures many health-related characteristics in this study. The algorithm described in this research might identify whether a person is at risk of developing chronic heart disease during the following ten years. After balancing the unbalanced dataset and feature selection, the accuracy attained was 83–84% using various models such as Logistic Regression, Random Forest Classifiers, and Linear Discriminant Analysis. The paper focuses on analyzing a varied and diverse dataset whereas other papers referenced and cited have drawbacks such as the size of the dataset being too small or geographically limited. These anomalies have been kept in consideration while working on this paper.
