Machine Learning Approach to Predict Treatment Outcome Using Shockwave Lithotripsy in Management of Urinary Stone

In Ontario, shock wave lithotripsy (SWL) is a regionalized resource and St. Michaels Hospital is one of only three centers in the province offering this service. As such, many of the patients travel a great distance to receive this noninvasive treatment. Our objective is to implement ensemble learning technique to predict treatment outcome based on the patients demographic information and stone characteristics. In order to construct a rigorous machine learning model that can be confidently applied to assist in decision making process, we built our model based on the whole dataset of patients ages over 18 for the years from 1998 to 2016. Our objective is to build a classification model to predict treatment outcome using SWL prior to making any decision on treatment modality. The success or failure was based on having retreatment plan for the same patient within less than 90 days of initial treatment. We also compared six machine learning algorithms performance on dataset in terms of their accuracy using t-test with 95% confidence interval. In addition, we performed a retrospective comparison of three shock wave lithotripsies (SWL) that has been used in SMH during the past two decades in terms of their successfulness. Furthermore, we looked at changing trends over time in terms of stone size, location, and patient BMI, and site of origin, gender, age, etc.