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@article{181722,
        author = {Bhakti Vinod Musmade and Prashant B. Kulkarni and Shubhangi P. Tidake},
        title = {Synergistic Impact of Mental Health, Physical Health, and Sleep  Duration on Heart Disease Prediction Using Ensemble Machine  Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {5273-5277},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181722},
        abstract = {cardiovascular disease prediction models tradition- ally prioritize clinical biomarkers while underutilizing behavioral determinants. This study demonstrates that integrating mental health, physical health, and sleep duration with clinical data using a stacked ensemble machine learning framework signifi- cantly improves prediction accuracy. We engineer novel interac- tion features (BMI×MentalHealth, SleepTime×PhysicalHealth) to capture synergistic effects, validated through ablation studies. Evaluated on 113,284 patients, our model achieves 92.12% accuracy (ROC AUC: 0.9787) - a 7.12% improvement over clinical-only models (85.00% accuracy). The findings establish behavioral factors as non-redundant predictors, contributing 18.7% of predictive power (p < 0.001). This work advocates for integrating multidimensional health indicators into clinical decision support systems.},
        keywords = {cardiovascular disease prediction, behavioral health analytics, stacked ensemble learning, machine learning interpretability, preventive cardiology},
        month = {June},
        }