Koppadi Sowmya , P S V Krishna , P Vamsi Krishna Raja 3 3 (July - September) An epidemic survey of recent years has reached a conclusion that virtually 179 million lives are lost annually due to disorders of the cardiovascular system and that these are the biggest killers worldwide The power to predict cardiac defects early may have potential to decrease morbidity and provide the opportunity for timely therapeutic interventions This study proposes an integrated computational model that incorporates traditional pattern recognition methods with neural networks using a hierarchical approach to model aggregation with transparent mechanisms of decision attribution Specifically the proposed system combines a multilayered feedforward neural network NN with batch normalization and dropout layers with nine different classification algorithmsLogistic Regression Decision Tree Random Forest Gradient Boosting Tree Support Vector Machine KNN XGBoost LightGBM and CatBoostand benchmarked them on various datasets The bestperforming models were then integrated using a hierarchical stacking model a Logistic Regression supermodel decides whether the test sets shall be combined or not and which members of the stacking set should indeed be in the final model Evaluated on the Cleveland cardiac dataset on UCI repository the proposed model can achieve 9016 classification accuracy 9643 sensitivity and receiver operating characteristic of area under the curve 09524 with 303 clinical records in 13 physiology attributes Oversampling with SMOTE helps to overcome the natural class imbalance found in the training partition and Shapleyvaluebased attribution provides for each patient a finegrained reason for each prognosis prediction The Streamlit frontend application provides a fully functional web application to help with a fast bedside risk assessment Empirical results show that the composite model architecture consistently outperforms individual classifiers by using the minimal possible rate of overlooked positive diagnosis one which proves to be very critical in clinical screening situations where negative diagnosis if overlooked has lifethreatening consequences Copyright (c) International Journal of Computational Science and Engineering Research . All rights reserved 2026