Disease Diagnosis Based on Machine Learning

Abstract

In recent years, disease diagnosis based on machine learning gradually become a hot topic. With the advancement of technology and medical standards, machine learning has also developed rapidly. This paper reviews the research background of disease diagnosis based on machine learning, the principles of machine learning, its applications in real life, and the challenges. First, the current situation of medical diagnosis is analyzed, including the limitations of traditional diagnosis methods and the advantages of machine diagnosis, Second, the key principles, processes, and core algorithms of machine learning are expounded. Then, the application of disease diagnosis based on machine learning in diseases such as breast cancer, Parkinson's disease, and osteoporosis is introduced in detail, and the performance of research methods and algorithms in the model is analyzed in combination with actual cases. Finally, the challenges faced by machine learning in the field of disease diagnosis and the prospects for the future are summarized. For example, there are challenges such as data quality, algorithm selection, and model interpretation. It looks forward to the development directions such as data privacy protection and sharing in the future, as well as technological innovation through multidisciplinary integration, providing references for relevant research.