Improved Performance Analysis for Cerebellar Ataxia disease Classification using AdaBoost

ABSTRACT

Artificial intelligence (AI) is the emerging field to diagnose and analyze chronic illnesses like Cerebellar Ataxia (CA), Spinocerebellar Ataxia (SCA), and Parkinson's disease. AI technologies such as machine learning and deep learning assist many doctors, diagnosis departments, and medical personnel in identifying and analyzing neurological disorders. Nowadays, AI used in most of the health care applications. Our research paper proffers an innovative approach to classify neurological disorders with various Machine learning algorithms. Existing research works experimented with machine learning algorithms like Support Vector machine and KNN, the performance of these algorithm is good, when the data is less and binary classified. In the proposed work, we have applied SVM, KNN, Decision tree and AdaBoost algorithms on the CA Data set. The performance of proposed methods exhibit improved accuracy when compared with the existing works. The results of the proposed work are tabulated for comparative analysis. We found that the AdaBoost algorithm shows the better classification result for Cerebellar Ataxia disease severity.