Abstract :

Heart Disease Is One Of The Leading Causes Of Mortality Worldwide, Making Early Detection And Diagnosis Crucial For Improving Patient Outcomes And Reducing Death Rates. Traditional Diagnostic Methods Are Often Expensive, Invasive, And Timeconsuming, Which Can Delay Timely Treatment. This Project Presents A Machine Learning-based Approach For Predicting Heart Disease Using Clinical And Demographic Patient Data. The Dataset Used In This Study Consists Of 1,888 Patient Records With 14 Significant Medical Features, Including Age, Sex, Chest Pain Type, Cholesterol Level, Blood Pressure, And Heart Rate. The Methodology Involves Exploratory Data Analysis (EDA), Data Preprocessing, Feature Scaling, And The Implementation Of The Random Forest Classification Algorithm. The Dataset Is Divided Into Training And Testing Sets To Evaluate The Model’s Performance And Accuracy. The Random Forest Model Achieved An Accuracy Of 95.5%, Demonstrating Strong Predictive Capability. Feature Importance Analysis Indicates That Factors Such As Chest Pain Type, Number Of Major Vessels, And Thalassemia Play A Significant Role In Determining Heart Disease Risk. Overall, The System Provides A Reliable And Efficient Decision-support Tool That Can Assist Healthcare Professionals In The Early Detection Of Heart Disease, Ultimately Improving Patient Care And Reducing Mortality Rates.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1274-1283

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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