Comparative Analysis on Machine Learning Approaches in Paralysis Disease Prediction

Abstract

Machine learning approaches availability and its automated solutions improved precision and reproducibility of the execution of crucial activities in a variety of sectors, including radiology, diagnostics, and many others, where it is already having a significant impact. Paralysis occurs when people are unable to make voluntary muscle movements. Paralysis is caused by a problem with the nervous system. The main focus of this article is to explore the idea of building a Paralysis Prediction and Monitoring Model with the help of machine learning techniques. It is used to determine the frequency of nerve stimulation in the affected region as well as monitor the stimulus and paralysis-precipitating factors. It also highlights various machine learning algorithms like Decision tree, K-Nearest Neighbors, Support Vector Machine, Logistic regression, Random Forest functionality in prediction of paralysis occurrence with the help of a accuracy graph and showcase the best machine learning approach in prediction of paralysis disease with the help of comparative analysis.