ALSFRS-R Score Prediction for Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that results in the gradual deterioration of motor abilities, leading to challenges in breathing, speaking, swallowing, and ultimately death, typically occurring within a few years. The symptoms of ALS can vary significantly from one individual to another, affecting various bodily functions and areas. To assess this wide range of symptoms, the Amyotrophic Lateral Sclerosis Functional Rating Scale - Revised (ALSFRS-R) is utilized. Predicting the ALSFRS-R score is clinically relevant for personalizing patient monitoring. To address this need, the Intelligent Disease Progression Prediction challenge was organized, tasking participants with developing novel methods to predict these scores using non-invasive sensor data that monitor some individual characteristics. The competition included two tasks that differed only in the way the ALSFRS-R questionnaires were completed: either by medical staff (task 1) or by the patient (task 2). Given the limited number of patients on the training set, it was decided to use a relatively simple model, Random Forest, and to preselect sensor features by retaining those most correlated with the outcome to be predicted. We selected the model with the lowest MAE estimated by cross-validation on the challenge training set. The competition results demonstrate that our method attained on the test set an average Mean Absolute Error (MAE) of 0.234 and 0.311, along with a Root Mean Square Error (RMSE) of 0.519 and 0.601 for tasks 1 and 2, respectively. Although the error may appear very low, this is because questionnaire values tend to remain constant from one visit to another, thus facilitating prediction.