ABSTRACT
Millions of elders are affected by Alzheimer’s disease (AD) worldwide. Many risk factors have a strong association with the prevalence of AD and its further progression. This chapter aims to design an AD risk assessment model using fuzzy cognitive map (FCM). Generally, the risk factors of AD are imprecise and not quantifiable. The FCM is an effective soft computing technique to model a clinical decision support system using qualitative data. The proposed system uses Hebbian learning algorithm to train the FCM model and predicts the AD risk level such as low risk, moderate risk, and high risk. The proposed AD risk assessment model is evaluated using nonlinear Hebbian learning algorithm with 30 sample records of risk factors and achieves 96% diagnostic accuracy. Out of 30 samples, 29 samples of risk factors have converged in the specified region, which derives a human being’s reasoning ability in evaluating the risk of AD.
