Past Issues

Cancer remains one of the leading causes of mortality worldwide, necessitating advanced techniques for early detection and accurate diagnosis. Traditional diagnostic methods are often time-consuming and expensive. This study explores the application of data mining techniques in cancer prediction, utilizing machine learning algorithms such as decision trees, support vector machines (SVM), artificial neural networks (ANN), and clustering approaches. Through analysing large medical datasets, these techniques help in identifying high-risk individuals and improving diagnostic precision. The findings suggest that data mining significantly enhances early detection, treatment planning, and survival prediction. Despite challenges like data privacy and class imbalances, advancements in artificial intelligence continue to refine these predictive models, making them more reliable and accurate. .

Review of Machine Learning Applications in Medical Diagnosis, Treatment Optimization, Personalized Care, Clinical Decision Support

Ketankumar Chaturbhai Patel, Dr. Satish Narayan Gurjar

CrossRef DOI URL : https://doi.org/10.31426/ijesti.2023.3.12.3952

This study critically examines the integration of machine learning (ML) techniques in medical diagnosis and treatment, highlighting their transformative potential and the challenges that accompany their adoption. Through a comprehensive literature review of recent advancements—from deep neural networks in medical imaging to reinforcement learning in treatment optimization—we demonstrate how ML enhances diagnostic accuracy, accelerates drug discovery, and enables personalized therapeutic regimens. Case studies, such as three-dimensional neural networks for early lung cancer detection and AI-driven platforms for COVID-19 management, illustrate tangible improvements in clinical outcomes and operational efficiencies..