HEART LENS DISEASE PREDICTION USING MACHINE LEARNING

In conclusion, the system demonstrates          the effectiveness of machine learning techniques for predicting heart disease by leveraging a diverse dataset derived from both publicly available and privately collected records containing clinical physiological and lifestyle attributes the implementation of feature selection techniques such as Chi-square, Analysis of Variance (ANOVA), and Mutual Information (MI) allowed the creation of optimized subsets SF-1, SF-2, and SF-3, ensuring improved classification performance. Ten machine learning algorithms, including Naive Bayes, Support Vector Machine,         XGBoost,  AdaBoost, Bagging Classifier, Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and a Voting Classifier, were applied to assess predictive accuracy. Evaluation was conducted with cross-validation, where the Voting Classifier showed consistent improvements across feature subsets, highlighting the strength of ensemble methods. Furthermore, a Stacking Classifier combining Boosted Decision Tree, ExtraTree, and LightGBM was introduced to further enhance results. SMOTE was employed exclusively in this configuration to address class  

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