Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis

Motivation: Improving the interpretability and intuitiveness of the machine learning models can help physicians in clinical decision-making. Goal(s): To investigate whether clinical and grey matter atrophy indicators can predict disability in relapsing-remitting multiple sclerosis (RRMS) and to enhance the interpretability and intuitiveness of a predictive model. Approach: Six machine learning classifiers were trained and tested to predict disability progression. Partial dependence plot (PDP) analysis and a Shiny web application were conducted. Results: The logistic regression model performed best, with an AUC of 0.950. PDP analysis showed which indicators had increased probabilities of disease progression. Finally, a Shiny web application was developed. Impact: The PDP analysis and Shiny web application can improve the interpretability and intuitiveness of the machine learning models to help physicians predict disability progression in RRMS.