Research Article
Optimal Prediction of Heart Disease Using Machine Learning Techniques with Logistic Regression Model
@INPROCEEDINGS{10.4108/eai.24-3-2022.2318558, author={Ghulab Nabi Ahmad and Hira Fatima and Shafiullah Shafiullah}, title={Optimal Prediction of Heart Disease Using Machine Learning Techniques with Logistic Regression Model}, proceedings={Proceedings of the 3rd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2022, 24-25 March 2022, New Delhi, India}, publisher={EAI}, proceedings_a={ICIDSSD}, year={2023}, month={5}, keywords={forecast; heart disease; symptoms; logistic regression}, doi={10.4108/eai.24-3-2022.2318558} }- Ghulab Nabi Ahmad
Hira Fatima
Shafiullah Shafiullah
Year: 2023
Optimal Prediction of Heart Disease Using Machine Learning Techniques with Logistic Regression Model
ICIDSSD
EAI
DOI: 10.4108/eai.24-3-2022.2318558
Abstract
One of the most difficult challenges in the health-care sector is to anticipate coronary artery disease. Heart disease seems to be more common in males than in women. The quantity of smokers smoked each day, as well as systolic and diastolic blood pressure, all increase the risk of heart disease. As a result, we propose to create an application that can forecast the risk of heart disease based on fundamental symptoms such as age, sex, pulse rate, and so on. The proposed solution makes use of the machine learning methodology logistic regression, which has been proved to be the most accurate and reliable. The model's performance is assessed using publicly available datasets such as the Cleveland Heart Disease Dataset (CHD), with logistic regression achieving the highest accuracy of 89.52 %. And an accuracy of 93.54 % for ROC_ AUC. We describe a predictive analytics-based technique for detecting heart disease in this research.