Framework Development for Evaluating Machine Learning Models in Health Predictive Analytics: A Multi-dimensional Approach for Clinical Translation and Ethical Implementation

Abstract This paper introduces a novel comprehensive framework for evaluating machine learning (ML) models in health predictive analytics that addresses the multifaceted challenges of implementing these technologies in clinical settings. While ML models show tremendous promise for transforming healthcare delivery, their adoption remains limited due to inadequate evaluation approaches that fail to capture the complex interplay between technical performance, clinical utility, operational feasibility, ethical considerations, and temporal stability. Our proposed Multi-dimensional Evaluation of Predictive healthcare Analytics and Learning Systems (MEPALS) framework integrates these critical dimensions into a unified evaluation methodology with quantitative scoring mechanisms that enable standardized assessment across different healthcare contexts. By emphasizing not only technical validation but also clinical translation, operational implementation, ethical considerations, and longitudinal performance monitoring, MEPALS provides healthcare institutions and researchers with a structured approach to comprehensively evaluate ML models throughout their lifecycle, potentially accelerating the responsible adoption of predictive analytics in healthcare settings.