Classification of Neurodegenerative Disorders Based on Major Risk Factors Employing Machine Learning Techniques

Abstract

Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for the classification of various diseases. Data mining technology provides a user-oriented approach to novel and hidden patterns in the data. The present study consisted of records of 746 patients collected from ADRC, ISTAART, USA. Around eight hundred and ninety patients were recruited to ADRC and diagnosed for Alzheimer's disease (65%), vascular dementia (38%) and Parkinson's disease (40%), according to the established criteria. In our study we concentrated particularly on the major risk factors which are responsible for Alzheimer's disease, vascular dementia and Parkinson's disease. This paper proposes a new model for the classification of Alzheimer's disease, vascular disease and Parkinson's disease by considering the most influencing risk factors. The main focus was on the selection of most influencing risk factors for both AD and PD using various attribute evaluation scheme with ranker search method. Different models for the classification of AD, VD and PD using various classification techniques such as Neural Networks (NN) and Machine Learning (ML) methods were also developed. It is observed that increase in the vascular risk factors increases the risk of Alzheimer's disease. It was found that some specific genetic factors, diabetes, age and smoking were the strongest risk factors for Alzheimer's disease. Similarly, for the classification of Parkinson's disease, the risk factors such as stroke, diabetes, genes and age were the vital factors.