A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States.

ABSTRACT: Despite the usefulness of artificial neural networks (ANNs) in the study of various complex problems, ANNs have not been applied for modeling the geographic distribution of tuberculosis (TB) in the US. Likewise, ecological level researches on TB incidence rate at the national level are inadequate for epidemiologic inferences. We collected 278 exploratory variables including environmental and a broad range of socio-economic features for modeling the disease across the continental US. The spatial pattern of the disease distribution was statistically evaluated using the global Moran's I, Getis⁻Ord General G, and local Gi* statistics. Next, we investigated the applicability of multilayer perceptron (MLP) ANN for predicting the disease incidence. To avoid overfitting, L1 regulariz

SUBMITTER: Mollalo A 

PROVIDER: S-EPMC6338935 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature