François Delbot

Background: This study focuses on Amyotrophic Lateral Sclerosis (ALS), a progressive neurodegenerative disease that affects motor neurons. ALS patients suffer from motor weakness, atrophy, spasticity, and difficulties in speaking, swallowing and breathing. Predicting the survival and progression of ALS is essential for optimising patient care, interventions and informed decision-making. Methods: Using the PRO-ACT (Pooled Resource Open-Access ALS Clinical Trials) database and clinical trial data from Exonhit Therapeutics, we applied machine learning techniques to predict disease progression based on ALS Functional Rating Scale (ALSFRS) scores and patient survival over one year of follow-up. Our models were validated through 10-fold cross-validation. Kaplan-Meier estimation was used to cluster patients effectively according to their profiles. To enhance the predictive accuracy of our models, we performed feature selection prior to analysis. Results: Logistic regression combined with feature selection yielded a Balanced Accuracy score of 76% (68.6% to 79.8% in each fold) on validation data and 76.33% on test data. Integrating model results with a Kaplan-Meier estimates, we identified four patient clusters, with a C-Index of 0.81 and an overall log-rank test p-value ≤ 0.0001. Our method demonstrated high accuracy in predicting ALS-FRS score progression at 3 months (RMSE = 2.88 and adjusted R² = 0.784 with random forest). This study showcases the potential of machine learning models to provide significant predictive insights in ALS, enhancing the understanding of disease dynamics and supporting patient care.