Hybrid Ensemble Machine Learning Algorithm for Heterogeneous Health Care Data Analytics

Abstract: Modern healthcare environments generate large volumes of heterogeneous data from multiple sources such as electronic health records, laboratory reports, medical imaging systems, and wearable sensors. Traditional single-model machine learning techniques often struggle to effectively analyse such diverse datasets due to differences in structure, scale, and data quality. To address these challenges, this paper proposes a hybrid ensemble machine learning framework for heterogeneous healthcare data analytics and disease prediction. The proposed system integrates multiple learning algorithms using ensemble strategies such as bagging, boosting, and stacking to improve prediction accuracy, robustness, and generalization performance. The framework supports multimodal data integration and incorporates feature selection and preprocessing techniques to handle noise, missing values, and class imbalance commonly present in healthcare datasets. Experimental analysis demonstrates improved reliability and predictive capability compared with conventional standalone models. The proposed approach provides an efficient decision-support framework for clinical risk prediction, disease diagnosis, and intelligent healthcare analytics in real-world medical environments.