Alzheimer's Disease Classification Based on Demographic Data and Machine Learning

Alzheimer's disease (AD) is a complex neurodegenerative disorder that presents significant challenges for early and accurate diagnosis. Early diagnostic and treatment strategies can help enhance the circumstances by slowing the progression of the illness and enhancing the patient and family's quality of life. Machine learning (ML) approaches have shown promise in improving the diagnosis and prognosis of Alzheimer's based on relevant risk factors. This paper aims to develop and evaluate a machine learning model for classifying Alzheimer's, mild cognitive impairment (MCI), and normal cognition (NC) using a diverse data set from the ADNI database. The model had high performance with a sensitivity rate of up to 97%, accuracy rate of up to 94%, and specificity rate of up to 96%. Moreover, none of the Alzheimer's cases were falsely detected as normal cognition but as mild cognitive impairment and none of the normal cognition cases were detected as Alzheimer's, but as mild cognitive impairment. The algorithm that has the highest number of true positive detections, which is 78 out of 85 Alzheimer's cases, is the decision tree algorithm. The performance of the system heralds a promising future for Alzheimer's diagnosis by machine learning with the aim of developing smart health systems.