Review and Analysis of Disease Diagnostic Models Using AI and ML

Abstract

Recently, disease prediction using diagnostic reports and images are one of the most popular applications of artificial intelligence (AI) and machine learning (ML). Several authors reported significant results in this area by combining cutting-edge hardware with AI and ML-based technologies. In this chapter, the authors present a review of different works carried for the prediction of several chronic diseases by researchers in last five years. Reported AI and ML based methodologies have been used to forecast chronic disease such as heart problems, brain tumors, asthma, diabetes, cholera, arthritis, liver diseases, kidney diseases, malaria, and leukemia. In the literature, the authors also discuss the different user interfaces which have been used to interact with real time AI and ML based disease prediction models. The authors have presented the detailed discussion of each paper including advantages, disadvantages, datasets, performance metrics such as precision, recall, accuracy and F1 score. In the final section, the survey concludes with a description of research gaps that can be addressed by future research attempts.