A machine learning approach for predicting therapeutic adherence to osteoporosis treatment

Abstract

Osteoporosis is a great disability burden with an expected cost increase of almost 50% by 2025. Due to its long term treatment, 50-70% of the patients withdraw from their osteoporosis medications within the first year of initiation. This necessitates an urgent need for improved osteoporosis and pharmacologic management tools most especially for pregnant women, postmenopausal women and the elderly to ensure therapeutic adherence of the patients during treatment. In this paper, we developed and tested accuracy of Machine Learning Models for predicting therapeutic adherence of patients to enable health professionals to compatibly decide on the therapeutic treatments and approaches for osteoporosis treatment and pharmacologic management of their patients. We were the first to develop and test Machine Learning Models for Predicting Therapeutic Adherence treatments. The ML Model accuracy results are summarized as classical metrics where the ExtraTree Model exhibited the highest accuracy of 100%, 85.0%, 94.5% on the training, testing and overall dataset respectively using Synthetic Minority Over-sampling Technique Support Vector Machine Learning (SMOTE-SVM).