Abstract:
As the population ages, Alzheimer’s disease is rapidly increasing, and the diagnosis
of the disease is still poorly understood. In comparison to cancer, 90% of patients become aware of their diagnosis, but only 45% of the people with Alzheimer’s are aware. Thus, the need for biomarkers for reliable diagnosis is tremendous to help in finding treatment for this serious disease. Hence, the main aim of this paper is to utilize information from baseline measurements to develop a statistical prediction model using multiple logistic
regression to distinguish Alzheimer’s disease patients from cognitively normal individuals. Our optimal predictive model includes six risk factors and two interaction terms and has been evaluated using classification accuracy, sensitivity, specificity values and area under the curve.
