Towards Explainable Machine Learning for Prediction of Disease Progression

Abstract

This research focuses on addressing the challenges surrounding interpretability of machine learning techniques in the field of prediction of disease progression. This paper summarizes the state-of-the-art in machine learning for disease progression modeling and challenges related to this context. Based on this state-of-the-art, we design and develop a pipeline consisting of data preparation, prediction, and explanation. Predictions are made using a deep recurrent neural network-based model which is followed by an integration of the LIME framework to provide explanations for each prediction. We demonstrate our pipeline by applying it to two diverse case studies on diabetes and Parkinson’s disease. Besides this, we compare the influence of three data imputation methods on predictive performance. Results of the comparison show that there is no statistically significant difference in performance due to different data imputation techniques. Furthermore, we provide a number of concrete recommendations and directions for future research, such as improving input flexibility of the prediction model and improving the visualization of generated explanations. Based on the results of this research, we conclude that the proposed pipeline achieves the goal of integrating a state-of-the-art prediction model and the LIME framework to make prediction of disease progression more transparent and interpretable.