Abstract :

A Major Cause Of Mortality In The World Is Cardiovascular Illness, And Early Detection Is Critical In Reducing The Rates Of Mortality. Cardiac Disease Is A Complex Medical Condition, And Its Exact Prognosis Cannot Be Made Because Of The Absence Of The Continuous Monitoring Opportunities. Based On The Dataset Of Heart Disease, Most Feature Selection Methods, Like ANOVA F-statistic (ANOVA FS), Chi-squared Test (Chi2 FS), And Mutual Information (MI FS), Were Applied To Determine The Relevant Predictors. Synthetic Minority Oversampling Technique (SMOTE) Was Applied To Address Data Imbalance To Enhance The Efficacy Of Models. In-depth Classification Methodology Was Used Using Different Machine Learning Models And Ensemble Techniques. A Stacking Classifier Which Combines Boosted Decision Trees, Extra Trees And LightGBM Has Scored Very High With 100 Percent Accuracy In All The Feature Selection Approaches. The High Performance Highlights The Effectiveness Of The Advanced Ensemble Learning To Generate Plausible Heart Disease Predictions, And There Are Possibilities Of Using Strong Feature Selection With Advanced Classification Models To Successfully Analyze Medical Data. The Approach Represents The Capacity To Promote Early Diagnosis And Patient Outcomes. Index Terms - Cardiovascular Disease, Heart Disease, Machine Learning App, ML Algorithms, SDG 3, SHAP, SMOTE.

Published:

21-2-2026

Issue:

Vol. 26 No. 2 (2026)

Page Nos:

121-133

Section:

Articles

License:

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

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