A Machine Intelligence Designed Bayesian Network Applied to Alzheimer's Detection Using Demographics and Speech Data

Abstract Bayesian networks (BNs) have classically been designed by two methods: expert approach (ask an expert for nodes and links) and data driven approach (infer them from data). An unexpected by-product of previous Alzheimer's / dementia research (presented at CAS2015) was yet another approach where the results of a hybrid design were used to configure a BN. A complex adaptive systems approach, (e.g. GA-SVM-oracle hybrid) can sift through the combinatorics of feature subset selection, yielding a modest set of only the most influential features. Then using known likelihoods of demographics associated to dementia, and assuming direct and independent influence of dementia upon speech features, the BN is specified. The conditional probabilities needed can be estimated with far fewer data than the traditional BN data-driven approach. Although BNs have advantages (intuitive interpretation and graceful handling of missing data) they also have challenges. We report initial implementation results that suggest the need to reduce continuous variables to discrete categories, and the still-remaining need to estimate a substantial number of conditional probabilities, remain challenges for BNs. We suggest some ways forward in the application of BNs with the objective of improving / refining Alzheimer's / dementia detection using speech.