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Heart Disease Prediction Using Logistic Regression
¹²³ Department of Computer Science Engineering-Data Science, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India.
Published Online: May-August 2025
Pages: 356-359
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250402048
This study presents a heart disease prediction system leveraging logistic regression. The process begins with preprocessing patient records by handling missing data, scaling features, and splitting datasets. Relevant input attributes such as cholesterol levels are utilized for model training and optimisation. The logistic regression model predicts the probability of heart disease based on input features. The system's output includes disease classification and is assessed using key performance metrics. This approach aims to enhance early detection and clinical decision- making efficiency.
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