Comparative Analysis of Machine Learning Models for Disease Prediction - Chotrani, Aakash (Journal of Science & Technology, 2022)
The increasing availability of health-related data and advancements in machine learning techniques have paved the way for the development of predictive models for disease diagnosis and prognosis. This study conducts a comprehensive comparative analysis of various machine learning models applied to disease prediction, aiming to identify the most effective approach for accurate and timely diagnosis. The research focuses on a diverse set of diseases, encompassing both communicable and non-communicable conditions, to ensure the generalizability of the findings. Multiple datasets containing relevant patient information, such as demographic details, medical history, and diagnostic tests, are utilized to train and evaluate the performance of various machine learning algorithms.