FINE-TUNING MACHINE LEARNING ALGORITHMS IN GERIATRIC HEALTHCARE: A FRAMEWORK FOR OPTIMIZING PREDICTIVE MODELS AND CLINICAL OUTCOMES

Abstract

Machine learning algorithms have shown remarkable potential in healthcare applications, yet their effective implementation for retiree populations requires specialized consideration and systematic fine-tuning. This article presents a comprehensive framework for optimizing machine learning algorithms specifically for healthcare applications targeting retired individuals. The article addresses the unique challenges inherent in retiree healthcare data, including multiple chronic conditions, medication interactions, and age-related factors, while proposing a structured approach to algorithm refinement. The methodology encompasses data preparation protocols, model selection criteria, and hyperparameter optimization strategies specifically tailored for geriatric healthcare applications. The article evaluates the framework's effectiveness through multiple case studies across different healthcare settings and present validation results demonstrating improvements in predictive accuracy and clinical relevance. The article also addresses critical ethical considerations, including privacy protection, bias mitigation, and model interpretability requirements for healthcare applications. The findings suggest that properly fine-tuned machine learning algorithms can significantly enhance healthcare delivery for retiree populations while maintaining robust privacy and fairness standards. This work contributes to the growing field of precision medicine for elderly care by providing a standardized, reproducible approach to developing and implementing machine learning solutions.