The use of machine learning for the diagnosis of various diseases

The application of machine learning techniques in the diagnosis of various diseases has garnered significant attention in recent years. This paper presents a comprehensive review of machine learning methodologies employed for disease detection, with a particular focus on kidney disease diagnosis. We analyze the effectiveness of various algorithms, including supervised and unsupervised learning models, in improving diagnostic accuracy and timeliness. By categorizing existing literature, we identify key trends, limitations, and emerging research directions within the field. Our findings indicate that machine learning not only enhances the early detection of chronic conditions but also aids in predicting disease progression, ultimately contributing to better patient outcomes. This review serves as a foundational resource for future studies aiming to leverage machine learning in healthcare settings.

کلیدواژه ها:

Machine learning ، Disease diagnosis ، Diagnostic algorithms