Multi perceptron network based model to classify the multi stages of Alzheimer's disease using clinical data

Alzheimer's is a neurodegenerative disease that diminishes the capability of individuals to perform their daily activities. It is an irreversible disease. Once it is started it cannot be cured, but early detection of the disease can help to slow down the progression of the disease by starting the treatment early. In this experiment, we use clinical study data available at the Alzheimer's disease neuroimaging initiative (ADNI) dataset to detect the different stages of Alzheimer's and forecast the duration required for conversion from mild cognitive impairment to Alzheimer's disease (AD) and also classification of the disease is done. The clinical signs of AD are rendered by age, patient education details, the progression rate of disease, and cognitive information. Various machine learning techniques like multi perceptron networks, random forest, SVM, and decision tree classifiers are used to do binary and multi-classification of AD, late mild cognitive impairment (LMCI), early cognitive impairment (EMCI), and cognitive control (CN). The multi perceptron network shows the best performance by giving an average accuracy of 99.24% for binary classification and 93% for multi class classification. The proposed model enables early detection and also predicts the different stages of Alzheimer's using the clinical dataset.