Abstract
Heart disease is recognized as one of the most critical health challenges in modern medical science and remains a major cause of mortality worldwide. Accurate and timely diagnosis plays a crucial role in preventing life-threatening cardiac conditions. This paper presents the design and implementation of a Heart Disease Diagnosis Prediction system using Machine Learning techniques. The proposed model analyzes patient medical parameters including age, cholesterol, resting blood pressure, chest pain type, maximum heart rate, blood sugar level, and other clinical attributes to determine the likelihood of cardiovascular disease. The system utilizes supervised learning algorithms such as Logistic Regression, Random Forest, and Support Vector Machine, trained and evaluated on a standard medical dataset. The objective of this project is to assist healthcare practitioners with a decision-support tool capable of predicting risk with significant accuracy and reliability. The results indicate that machine learning provides an effective approach to identifying heart disease at an early stage, enhancing clinical efficiency and patient care. The system can be extended with real-time sensor integration, advanced deep learning models, and deployment on cloud platforms for large-scale healthcare applications.
Keywords
Machine Learning, Classification, Heart Disease Prediction, Healthcare Analytics, Data Mining, Random Forest, Logistic Regression.
Citations
IRE Journals:
Eram Iqbal, Sandeep S N, Shankar S, Shifa Hani, Abdul Rahaman "Heart Disease Diagnosis Prediction" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 986-989 https://doi.org/10.64388/IREV9I6-1712825
IEEE:
Eram Iqbal, Sandeep S N, Shankar S, Shifa Hani, Abdul Rahaman
"Heart Disease Diagnosis Prediction" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712825
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