PERSONALIZED HEALTHCARE OPTIMIZATION THROUGH MACHINE LEARNING MODELS FOR PREDICTIVE DIAGNOSIS AND TREATMENT PATHWAYS

Surya Krishna Kumar

Authors

Surya Krishna Kumar India Author

Keywords:

Personalized healthcare, machine learning, predictive diagnosis, treatment pathways, patient outcomes, healthcare optimization

Abstract

The advent of machine learning (ML) in healthcare has sparked substantial advancements in predictive diagnosis and personalized treatment pathways, paving the way for improved patient outcomes and optimized resource allocation. This paper examines the role of machine learning models in personalizing healthcare, focusing on how predictive algorithms can enhance early diagnosis, personalize treatment plans, and provide insights into patient outcomes. Through a structured review of current literature, this paper identifies prominent ML approaches in healthcare, including supervised and unsupervised learning models, their applications in predictive diagnostics, and the challenges of integrating these models in clinical settings. Results indicate that ML-based predictive models are increasingly accurate in diagnosing conditions and predicting patient response to treatments, demonstrating a transformative potential for patient-specific healthcare optimization. However, technical and ethical challenges, including data quality, bias, and interpretability, remain substantial barriers to widespread clinical adoption.

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