Enhancing Machine Learning Performance for ALS Classification Using Feature Selection Technique

Background: Electromyogram (EMG) is considered for the diagnosis of amyotrophic lateral sclerosis (ALS). We investigated how frequently an initial electrophysiological evaluation is performed in ALS patients. Objective: We aimed to identify ALS cases using the feature selection algorithm (FSA). A pattern of feature involvement was expected in the early phase of ALS based on the typical consistent involvement of a few particular muscles in the late stages. Methods: We used a clinical and EMG dataset of 36 patients (21 ALS patients and 15 healthy) for analysis. The present approach used a Random Forest (RF)-based machine learning (ML) algorithm for the prediction of ALS patients. The goal of this study was to determine how feature selection techniques can enhance machine learning algorithms that can be used in real-time applications. Findings: The experimental findings demonstrate the superior performance of the proposed model. This work demonstrates how machine learning models can offer important predictive insights in ALS, improving knowledge of disease dynamics and aiding patient care. According to the comparison results, the RF model utilizing the feature selection algorithm demonstrated a positive improvement, and the accuracy of the RF model with and without the feature selection technique was found to be 87.50% and 75%, respectively. Conclusions: The algorithm improved the model accuracy from 75% to 87.50%, resulting in an absolute increase of 12.5 percentage points in classification accuracy. This study found that fibrillation, positive sharp waves (PSW), and fasciculation features were high-yielding in EMG of ALS cases.