Machine learning algorithms for healthcare
The study examines why ML remains hard to use directly in medical settings through an analysis of data quality gaps plus ethical and interpretability problems, and examines possible future directions alongside recommended methods to upgrade present restrictions.
Abstract
Machine learning technology has led to major changes in how healthcare works. Machine learning tools bring new ways to improve every part of healthcare delivery from detecting conditions to planning treatments and checking patients. New data gathering methods alongside stronger computer systems and smarter programs make ML systems more valuable for medical use. This publication studies how machine learning programs help healthcare systems solve complicated health problems. Our investigation shows how ML algorithms recognize medical conditions including cancer, diabetes, and heart diseases while enabling doctors to personalize patient care. The study examines why ML remains hard to use directly in medical settings through an analysis of data quality gaps plus ethical and interpretability problems. Our research explores specific instances where ML technology delivers outstanding results including diagnostic radiology, genetic research, and illness forecasting. This paper examines possible future directions alongside recommended methods to upgrade present restrictions. We use emerging ML research and real-world examples to show how machine learning boosts medical care and increases healthcare benefits for patients and systems. Using machine learning in healthcare shows great promise for the future even though problems remain to be solved.