Research on Diagnosis Method of Common Knee Diseases Based on Subjective Symptoms and Random Forest Algorithm

Abstract

Knee diseases are common diseases in the elderly, and timely and effective diagnosis of knee diseases is essential for disease treatment and rehabilitation training. In this study, we construct a diagnostic model of common knee diseases based on subjective symptoms and random forest algorithm to realize patients' self-initial diagnosis. In this paper, we first constructed a questionnaire of subjective symptoms of knee, and set up a questionnaire system to guide users to fill out the questionnaire correctly. Then clinical data collection is carried out to obtain clinical questionnaire data. Finally, the diagnostic analysis of three common diseases of knee joint is carried out by random forest machine learning method. Through leave-one-out cross validation, the accuracy of meniscus injury, anterior cruciate ligament injury and knee osteoarthritis diseases are 0.79, 0.84, 0.81 respectively; the sensitivity is 0.79, 0.84, 0.88 respectively; and the specificity is 0.80, 0.84, 0.79 respectively. The results show that the method can achieve a good effect of self-diagnosis, and can provide a knee joint disease screening a convenient and effective approach.