Alzheimer’s disease prediction using three machine learning methods

Shaymaa Taha Ahmed, Suhad Malallah Kadhem


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.


Alzheimer’s disease; Gene expression; Information gain; Microarraytechnology; Support vector machine

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