An Enhanced Multiple Sclerosis Disease Diagnosis via an Ensemble Approach.

ABSTRACT: Multiple Sclerosis (MS) is a disease attacking the central nervous system. According to MS Atlas's most recent statistics, there are more than 2.8 million people worldwide diagnosed with MS. Recently, studies started to explore machine learning techniques to predict MS using various data. The objective of this paper is to develop an ensemble approach for diagnosis of MS using gene expression profiles, while handling the class imbalance problem associated with the data. A hierarchical ensemble approach employing voting and boosting techniques is proposed. This approach adopts a heterogeneous voting approach using two base learners, random forest and support vector machine. Experiments show that our approach outperforms state-of-the-art methods, with the highest recorded accuracy being 92.81

SUBMITTER: Torkey H 

PROVIDER: S-EPMC9316893 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature