International Journal of Science, Engineering and TechnologyAuthors: Assistant Professor Mrs. G. Lakshmi Durga, Lakka Pranavi, Kakarla Veera Venkata Lakshmi Poojitha,, Pagolu Naga Divya,, Chiluvuri Sucharitha
Abstract: Heart disease is one of the main causes of death around the world. This shows the urgent need for effective prediction systems to prevent serious heart events. This study introduces a Hybrid Stacked Machine Learning Model (HSMLM) for predicting heart disease risk. It combines traditional and ensemble-based classifiers to improve diagnostic accuracy and reliability. Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), XGBoost and K-Nearest Neighbour (KNN) are the algorithms used by the framework. These algorithms are used in an optimized stacked ensemble environment. The features are selected by determining their importance with Chi-Square, ANOVA and Mutual Information to get the most important clinical factors. Class imbalance is also addressed to enhance performance with the Synthetic Minority Oversampling Technique (SMOTE) and cross-validation. The modelling in analyzed against well-known standard datasets with UCI and Kaggle heart disease datasets to confirm validity and attain 92% accuracy, 92% F1-score and 94% ROC-AUC, which outperformed individual models by 3-4%. This demonstrated that using a hybrid model provides significant improvement with predictive credibility with reasonable interpretative clarity. It shows progress in approach as an innovative decision-support tool for anticipated early diagnosis support. The HSMLM approach provided opportunities for both practical decision-making and clinical inference with the goal of reducing deaths caused by heart disease while improving patient outcomes.
