Keywords: Multiple Sclerosis, 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.
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