Predicting and Analyzing Osteoarthritis Patient Outcomes with Machine Learning

Sammanfattning: The use of machine learning as a tool in medicine is increasing and has provided new avenues for research into a number of diseases. Creating better predictive models for these diseases could provide opportunities for better care, which we have applied to osteoarthritis, a degenerative disease that affects a large part of the older population. We have sought to answer ``Is it possible to predict patient outcomes?'' and ``What factors contribute to the patient outcome?'' by constructing and evaluating machine learning models. In order to do this, a dataset containing 75,366 patients who have participated in an osteoarthritis treatment program was used and analyzed. The selection of models included neural networks, logistic regression, and gradient boosting machines among others in order to capture the performance of several types of machine learning models. Our results show that it is possible to predict patient outcomes on a test set with ~60% accuracy.