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Abstract

Disclosed are embodiments of method and system to predict health condition of a human subject. The method comprises receiving historical human-subject related data including records corresponding to multiple data views. The method estimates one or more latent variables based on: a first value indicative of count of records in a cluster, a second value indicative of count of records, and a third value indicative of a parameter utilizable to predict a fourth value. The fourth value corresponds to selection probability of a D-vine pair copula family, of a D-vine mixture model, utilizable to model a cluster. The method generates the D-vine mixture model based on the estimated one or more latent variables. The method further comprises receiving multi-view data of a second human subject and predicting health condition of the second human subject based on the multi-view data using a classifier trained based on the estimated latent variables.

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