A Double Layer Dementia Diagnosis System Using Machine Learning Techniques
Studies show that dementia is highly age-associated. Early diagnosis can help patient to receive timely treatment and slow down the deterioration. This paper proposed a hierarchical double layer structure with multi-machine learning algorithms for early stage dementia diagnosis. Fuzzy cognitive map (FCM) and probability neural networks (PNNs) were adopted to give initial diagnosis at based-layer, and then Bayesian networks (BNs) was used to make a final diagnosis at top-layer. Diagnosis results, “proposed treatment” and “no treatment required” can be used to provide self-testing or secondary dementia diagnosis to medical institutions. To demonstrate the reliability of the proposed system, a clinical data provided by the Cheng Kung University Hospital was examined. The accuracy of this system was as high as 83%, which showed that the proposed system was reliable and flexible.
KeywordsDementia diagnosis machine learning probability neural networks fuzzy cognitive map Bayesian networks
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