11. Predicting the Occurrence of Diabetes Using Analytics
Ravi S. Behara, Florida Atlantic UniversityAnkur Agarwal, Florida Atlantic UniversityVinaya Rao, Methodist University Hospital Transplant InstituteChristopher Baechle, Florida Atlantic University
In this chapter, we investigate diabetic patient classification using multilayer perceptron (MLP) and Bayesian networks (BNs). The models were evaluated on the basis of accuracy, root mean squared error (RMSE), and area under receiver operating curve (AUC). The dataset used for evaluation was CDC-NHANES 2011–2012, which is a survey of approximately 5,000 individuals’ health and nutritional status as collected by the Centers for Disease Control on a yearly basis. In all classification ...