Research on several major diseases based on machine learning models

Abstract

Due to the continuous growth of disease types and past cases, it is more and more difficult to diagnose diseases only by manpower. Machine learning is a model mechanism that is sensitive to data and relies on a large amount of data to complete training. It is very suitable for medical diagnosis. Many scholars have tried to use ML to develop medical diagnosis systems, but they are basically not used in the real world at this stage. This article reviews the work related to medical detection of three major diseases (heart disease, cancer, and COVID-19), aiming to summarize previous experiences to help future scholars conduct research. Specifically, this paper summarizes the research status of the prediction of these three types of diseases based on machine learning methods, evaluate the accuracy and universality of the corresponding prediction models based on time as a clue, and use a comparative method to find out the progress researchers have made in this area and limitations still exist at this stage. And at the end of the article, the results and some potential work fields of the future in these studies are summarized.