Diagnosis, Prediction, and Risk Factor Analysis of Multiple Sclerosis Disease using Quantum Machine Learning Classifier
Abstract
Multiple sclerosis (MS) is a condition that affects the brain or central nervous system, which can result in issues with the optic nerve, brain, and spinal cord. Multiple sclerosis is thought to affect 2.8 million people. This research focuses on the diagnosis of multiple sclerosis using Quantum machine learning (QML) classifiers. Several models have been created utilizing various data sets, such as clinical and magnetic resonance imaging (MRI) data. The quantum machine learning has emerged by the merging of classical machine learning and quantum information processing as a result of the superiority and advancement of quantum computing in various fields (such as encryption, machine learning, and healthcare). The experimental results present a comparative analysis of classical machine learning and quantum machine learning algorithms for the problem under consideration.
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