Learning Risk Factors from Homogeneous and Heterogeneous Survival Data: A Hybrid Machine Learning Framework with Applications to Heart Failure and Leprosy

Background/Objectives: Machine learning-based survival analysis methods often outperform classical approaches but entail greater methodological complexity and computational cost. This study proposes a unified three-component framework for selecting optimal survival modeling strategies by jointly considering data characteristics and research objectives. Methods: The framework comprises variable selection, classical and tree-based machine learning survival models, and performance evaluation using both time-aggregated and time-resolved metrics. It is validated on two distinct datasets of heart failure and leprosy, representing homogeneous and heterogeneous survival structures. Results: It is found that the optimal machine learning approach and variable selection are data dependent. For the heart failure dataset, a bagging survival tree achieves optimal performance, using only six predictors. For the leprosy dataset, the full predictor model yields the best performance, while the computationally efficient random survival forest performs comparably to the more complex bagging approach. Beyond model selection, the framework explicitly incorporates secondary objectives, including parsimony and computational efficiency, and supports the derivation of interpretable decision pathways for risk stratification. Conclusions: Heart failure patients with an ejection fraction below 20% should be considered as the highest risk group, whereas leprosy patients with symptom duration under one month can be regarded as the lowest risk category. In short-term prediction settings (less than 50 days/one year for heart failure/leprosy), the classical Cox proportional hazards model performs comparably to machine learning methods and may be chosen as the preferred method for simplicity. Overall, the proposed multi-component framework enables context-aware model selection by balancing predictive performance, model complexity, and temporal considerations.