Visual Prediction of the Progression of Spinocerebellar Ataxia Type 3 Based on Machine Learning

Abstract

Background: Spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is a clinically heterogeneous and progressive condition. Evaluation of its progression will contribute to clinical management and genetic counseling. Objective: The objective of this study was to provide a visualized interpretable prediction of the progression of SCA3/MJD based on machine learning (ML) methods. Methods: A total of 716 patients with SCA3/MJD were included in this study. The International Cooperative Ataxia Rating Scale (ICARS) and Scale for the Assessment and Rating of Ataxia (SARA) scores were used to quantitatively assess disease progression in the patients. Clinical and genotype information were collected as factors for predicting progression. Prediction models were constructed with ML algorithms, and the prediction results were then visualized to facilitate personalizing of clinical consultation. Results: The CAG repeat length of ATXN3 and its product with age, the duration of disease, and age were identified as the 4 most important factors for predicting the severity and progression of SCA3/MJD. The SVM-based model achieved the best performance in predicting the total ICARS and SARA scores, with accuracy (10%) values of 0.7619 for the SARA and 0.7042 for the ICARS. To visualize the predictions, line charts were used to show the expected progression over the next decade, and radar charts were used to show the scores of each part of the ICARS and SARA separately. Conclusion: We are the first group to apply ML algorithms to predict progression in SCA3/MJD and achieved desirable results. Visualization provided personalized predictions for each sample and can aid in developing clinical counseling regimens in the future.