MACHINE LEARNING FRAMEWORK FOR MEDICAL DATA PREDICTION AND PATIENT CARE

Abstract

Abstract: The rapid growth of healthcare data and digital technologies has transformed the medical industry. Machine Learning (ML) techniques have emerged as powerful tools for predicting diseases, improving patient care, reducing diagnostic errors, and supporting clinical decision-making. This research paper proposes a Machine Learning Framework for Medical Data Prediction and Patient Care that integrates data collection, preprocessing, predictive modeling, and intelligent healthcare management. The framework utilizes supervised and unsupervised learning algorithms to analyze medical datasets and provide accurate predictions for diseases and treatment outcomes. The study highlights the importance of healthcare analytics, predictive modeling, and AI-driven patient monitoring systems in improving healthcare quality and operational efficiency. The findings indicate that machine learning models significantly enhance disease prediction accuracy, reduce healthcare costs, and improve patient satisfaction. Keywords: Machine Learning, Medical Data Prediction, Healthcare Analytics, Artificial Intelligence, Patient Care, Predictive Modeling, Clinical Decision Support System.