Alzheimer’s disease prediction using three machine learning methods

Abstract

Alzheimer's disease (AD) is the most common incurable neurodegenerative illness, a term that encompasses memory loss as well as other cognitive abilities. The purpose of the study is using precise early-stage gene expression data from blood generated from a clinical Alzheimer's dataset, the goal was to construct a classification model that might predict the early stages of Alzheimer's disease. Using information gain (IG), a selection of characteristics was chosen to provide substantial information for distinguishing between normal control (NC) and early-stage AD participants. The data was divided into various sizes; three distinct machine learning (ML) algorithms were used to generate the classification models: support vector machine (SVM), Naïve Bayes (NB), and k-nearest neighbors (K-NN). Using the WEKA software tool and a variety of model performance measures, the capacity of the algorithms to effectively predict cognitive impairment status was compared and tested. The current findings reveal that an SVM-based classification model can accurately differentiate cognitively impaired Alzheimer's patients from normal healthy people with 96.6% accuracy. As discovered and validated a gene expression pattern in the blood that accurately distinguishes Alzheimer's patients and cognitively healthy controls, demonstrating that changes specific to AD can be detected far from the disease's core site.