Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis (MS), a chronic autoimmune neurological condition. It disrupts signals between the brain and body, causing symptoms including tiredness, muscle weakness, and difficulty with memory and balance. Traditional methods for detecting MS are less precise and time-consuming, which is a major gap in addressing this problem. This gap has motivated the investigation of new methods to improve MS detection consistency and accuracy. This paper proposed a novel approach named FAD consisting of Deep Neural Network (DNN) fused with an Artificial Neural Network (ANN) to detect MS with more efficiency and accuracy, utilizing regularization and combat over-fitting. We use gene expression data for MS research in the GEO GSE17048 dataset. The dataset is preprocessed by performing encoding, standardization using min-max-scaler, and feature selection using Recursive Feature Elimination with Cross-Validation (RFECV) to optimize and refine the dataset. Meanwhile, for experimenting with the dataset, another deep-learning hybrid model is integrated with different ML models, including Random Forest (RF), Gradient Boosting (GB), XGBoost (XGB), K-Nearest Neighbors (KNN) and Decision Tree (DT). Results reveal that FAD performed exceptionally well on the dataset, which was evident with an accuracy of 96.55% and an F1-score of 96.71%. The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.