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Schistosomiasis mansoni is a disease with social and behavioral characteristics. Snails of the Biomphalaria species, the disease's intermediate host, use water as a vehicle to infect man, the disease main host. In Brazil, six million people are infected. From 1995 to 2005, more than a million positive cases were reported, 27% of them in the state of Minas Gerais. The objective of this paper is to estimate the prevalence risk of schistosomiasis, in terms of remote sensing, climate, socioeconomic, or neighborhood variables or a subset of them. We present two approaches for modeling and classifying the infection risk: a global and a regional one, both of them using the aforementioned variables. In the first approach, a unique regression model was generated and used to estimate the disease risk for the entire state. In the second approach, the state was divided in four regions, and a model was generated for each of them. The first model obtained 47.2% of overall accuracy (AC) and the second achieved 62.4%, which were considered unsatisfactory. To improve these results, the concept of imprecise classification, defined in terms of the standard deviation of estimates and several reliability levels, is used for the generation of two imprecise classification maps. The AC for the imprecise classification was 83.8% for the global model and 91.9% for the regional one, which were now considered acceptable. Particularly, regionalization has proven to be a good guideline to follow in future works involving geographical aspects and large data heterogeneity.