Classification of Alzheimer's Disease and Parkinson's Disease by Using Machine Learning and Neural Network Methods

Data mining is a fast evolving technology, is being adopted in biomedical sciences and research. Data mining in medicine is an emerging field of high importance for providing prognosis and a deeper understanding of the classification of neurodegenerative diseases. Given a data set of consists of 487 patients records collected from ADRC, USA. Around eight hundred and ninety patients were recruited to ADRC and diagnosed for AD (65%) and PD (40%), according to the established criteria. In our study we concentrated particularly on the major risk factors which are responsible for Alzheimer's disease and Parkinson's disease. This paper proposes a new model for the classification of Alzheimer's 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 and PD using various classification techniques such as Neural Networks (NN) and Machine Learning (ML) methods were also developed. 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.