Personalized Healthcare Decision Support Using Machine Learning

Abstract

Personalized healthcare has emerged as a critical approach to improving clinical outcomes by tailoring medical decisions to individual patient characteristics. This study aims to develop an intelligent healthcare decision support system using machine learning techniques to provide personalized predictions and treatment recommendations. The proposed system utilizes patient data obtained from electronic health records, including demographic information, clinical measurements, and historical diagnoses. After data preprocessing and feature engineering, supervised machine learning models such as Random Forest and Gradient Boosting are trained to predict disease risk and support clinical decision-making. Model performance is evaluated using standard metrics including accuracy, precision, recall, and area under the ROC curve. The results demonstrate that machine learning-based personalized decision support systems significantly improve prediction accuracy compared to traditional rule-based approaches. The findings highlight the potential of machine learning to enhance early disease detection, optimize treatment strategies, and assist healthcare professionals in delivering patient-centered care. In conclusion, the study confirms that integrating machine learning into healthcare decision support systems can lead to more accurate, efficient, and personalized medical decisions, while emphasizing the need for data privacy, model interpretability, and ethical considerations for real-world deployment.