Predicting Progression of ALS Disease with Random Frog and Support Vector Regression Method

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that involves the degeneration and death of the nerve cells in brain and spinal cord that control voluntary muscle movement. This disease can cause patients struggling with a progressive loss of motor function while typically leaving cognitive functions intact. This paper presents a novel predication method that combines a dimension reduction (integrating partial least square into random frog algorithm) with support vector regression to predict the progression of ALS in the next 3–12 months according to the data collected from the patients over the latest three months. The experiment on the actual data from the PRO-ACT database indicates that the proposed method is effective and robust and can predict the clinical outcome by means of the slope of ALS progression, as measured using the ALS functional rating scale (ALSFRS) and the score used for monitoring ALS patients. Especially, the features selected can effectively distinguish the clinical outcome targets. It is of great benefit to aid clinical care, identify new disease predictors and potentially significantly reduce the costs of future ALS clinical trials.