TITLE:
Alzheimer’s Disease: The Relative Importance Diagnostic
AUTHORS:
Maryam Habadi, Christos P. Tsokos
KEYWORDS:
Alzheimer’s Disease, Multiple Logistic Regression, Predictive Model, Classification Accuracy
JOURNAL NAME:
Advances in Alzheimer's Disease,
Vol.9 No.4,
December
31,
2020
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.