Prediction and Analysis of Muscular Paralysis Disease using DWT and Hybrid Features

Genetic advancements have shown that ALS is not a single entity but consists of a collection of syndromes in which the motor neurons degenerate. Together with these multiple genetic etiologies, there is a broad variability in the disease’s clinical manifestations in terms of the age of symptom onset, site of onset, rate and pattern of progression, and cognitive involvement. In this paper, prediction of human paralysis is done based on extraction of features from the ALS dataset samples. The classification has been carried by two different Machine Learning based algorithms i.e., Gradient Boosting (GB), and neural Network (NN). The standard data set such as ALS has been used for this purpose. The classifier model has used 80% data as a training set and the remaining 20% of data as the test set. The result shows that GB and NN perform better with an accuracy of 98%. Based on the desired accuracy, this classification model serves better compared with existing models.