A Study of Predicting Ailments Using Machine Learning and it's Contrast to the Deep Learning Algorithm Design

This Artificial Intelligence has long influenced all aspects of human life. Its subsets, machine learning and deep learning, have rapidly advanced research across various domains. As ML and DL impact cross-disciplinary studies, evaluating their performance and computational effectiveness becomes crucial. In the medical field, both approaches have made significant contributions to understanding human anatomy and detecting irregularities. This paper presents a comparative analysis of five algorithm designs, aiming to understand the structure and effectiveness of both AI methods. On larger datasets, ML models often exhibit slower performance, whereas DL models surpass them in accuracy and other performance metrics. A comprehensive study, supported by visual analysis, is presented. However, in real-world applications, challenges such as interpretability, bias in data, and computational cost can impact deployment and performance, especially in healthcare contexts.