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The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.

Tabular-based clinical risk prediction models are extensively applied in medical decision support systems; however, two major challenges often reduce their reliability: predictions that contradict basic clinical logic and poorly calibrated probability outputs that weaken threshold-based decision making. This study investigates explainable binary risk prediction using the processed Cleveland subset of the UCI Heart Disease dataset as a public clinical benchmark. A lightweight and CPU-efficient pipeline is proposed by employing an XGBoost classifier integrated with monotonic constraints on clinically relevant features, followed by probability calibration through post-hoc methods, including Platt scaling, temperature scaling, and isotonic regression on a separate validation set. Model performance is assessed in terms of discrimination capability using AUROC, AUPRC, F1-score, sensitivity, and specificity, while probability reliability is evaluated using ECE and Brier score metrics. A monotonicity audit is also conducted through counterfactual feature sweeps to measure violation rates. In addition, the model is applied for risk stratification into low-, medium-, and high-risk categories with corresponding event-rate reporting. The findings demonstrate that isotonic regression improves probability reliability without degrading discrimination performance. Furthermore, the monotonicity audit reveals no observed violations for constrained features. Overall, the integration of monotonic constraints and probability calibration produces more decision-ready risk estimates for threshold-based clinical decision support while maintaining transparency through SHAP-based analysis.