Heart Disease Prediction Using Machine Learning

Sujan Reddy Siddireddy; B.Sri Madhav; S.Akthar Hussain; Voodara Devender

Heart Disease Prediction Using Machine Learning

Author(s)	Mr. Sujan Reddy Siddireddy, B.Sri Madhav, S.Akthar Hussain, Voodara Devender
Country	India
Abstract	Heart disease continues to be one of the leading causes of mortality worldwide, accounting for a significant percentage of global deaths each year. Early detection and accurate prediction of heart-related conditions are therefore essential for effective treatment and preventive healthcare. This project, “Heart Disease Prediction Using Machine Learning,” focuses on developing an intelligent prediction system that assesses an individual’s risk of heart disease based on multiple medical and lifestyle factors. The system considers important input attributes such as age, region of residence, gender, difficulty in walking, level of physical activity, diabetes, skin cancer, smoking habits, alcohol consumption, and history of previous heart attacks. Using these features, the project employs the Logistic Regression algorithm — a well-established and interpretable supervised learning model — to classify whether a person is likely to have heart disease. After preprocessing the dataset, including data cleaning, feature encoding, and normalization, the model is trained and tested to achieve an accuracy of 84%, demonstrating reliable predictive performance. To extend its real-world applicability, the system is integrated with a Hospital Management System (HMS) that facilitates seamless communication between patients and doctors. Patients can log in to their accounts to book appointments by selecting the doctor, time slot, symptoms, and other required details. Doctors can log in using their credentials to view appointments, manage patient data, and post prescriptions, which are directly visible to patients under their respective accounts. Additionally, the Heart Disease Prediction feature is accessible through the patient dashboard, allowing users to instantly assess their risk level by entering the required health parameters. This integration of machine learning with hospital data management significantly enhances the healthcare workflow. preventive healthcare by enabling early medical intervention.Overall, the proposed system demonstrates the potential of artificial intelligence in transforming conventional healthcare practices into intelligent, data- driven solutions that are efficient, reliable, and accessible — ultimately contributing to improved patient outcomes and smarter hospital operations.Heart Disease Prediction Using Machine Learning
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-23

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Advanced International Journal for Research

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Heart Disease Prediction Using Machine Learning

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