MACHINE LEARNING TECHNIQUES FOR HEALTHCARE DATA ANALYSIS

Abstract

Abstract: Machine Learning (ML) has emerged as one of the most transformative technologies in the healthcare sector. The rapid growth of healthcare data from hospitals, laboratories, wearable devices, and electronic health records (EHRs) has created opportunities for intelligent data analysis and predictive healthcare systems. Machine learning techniques help healthcare organizations improve disease diagnosis, patient monitoring, treatment planning, and operational efficiency. This research paper examines major machine learning techniques used in healthcare data analysis, including supervised learning, unsupervised learning, reinforcement learning, and deep learning methods. The study also discusses applications of ML in disease prediction, medical imaging, personalized medicine, drug discovery, and healthcare management. Furthermore, the paper highlights challenges such as data privacy, interpretability, and ethical concerns. The findings indicate that machine learning significantly enhances healthcare decision-making and patient outcomes while reducing costs and human errors. Keywords: Machine Learning, Healthcare Data Analysis, Artificial Intelligence, Deep Learning, Disease Prediction, Electronic Health Records.