• R.Lavanya Bai
• S.Shri Preetha
• Rejitha D

Machine Learning (ML) has become a pivotal technology in the transformation of the healthcare sector, enabling innovative solutions for disease diagnosis, prediction, and treatment planning. This study consolidates insights from multiple research works to present a comprehensive view of ML applications in healthcare, covering areas such as medical imaging, electronic health record analysis, genetic data interpretation, and real-time health monitoring. Widely used ML algorithms—including Support Vector Machines (SVM), Random Forests, Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN)—demonstrate promising results in extracting meaningful patterns from complex and diverse datasets. However, the integration of ML into clinical practice still faces significant challenges, including data fragmentation, lack of transparency in model predictions, and limited infrastructural support. Despite these limitations, ML continues to drive advancements in personalized medicine, clinical decision support systems, and remote care, highlighting its potential to enhance the efficiency and quality of modern healthcare delivery.

Machine Learning, Healthcare, Disease Prediction, Medical Imaging, Decision Support System.

"PREDICTIVE ANALYTICS AND APPLICATIONS OF MACHINE LEARNING IN MEDICINE: TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.f128-f130, June-2025, Available :http://www.jetir.org/papers/JETIR2506514.pdf

"PREDICTIVE ANALYTICS AND APPLICATIONS OF MACHINE LEARNING IN MEDICINE: TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppf128-f130, June-2025, Available at : http://www.jetir.org/papers/JETIR2506514.pdf