title: Estimating probabilities of diabetes mellitus using neural networks.
abstract: Classification problems are often encountered in medical diagnosis. This paper presents an introduction to classification theory and shows how artificial neural networks can be used for classification. We also map out a bootstrapped procedure for interval estimation of posterior probabilities. The entire procedure is illustrated using the diabetes mellitus data in Pima Indians.
authors.affiliation: College of Business, Kent State University Kent, OH 44242-0001, USA. mshanker@kent.edu
authors.forename: M
authors.initials: M
authors.lastname: Shanker
authors.forename: M Y
authors.initials: MY
authors.lastname: Hu
authors.forename: M S
authors.initials: MS
authors.lastname: Hung
mesh_terms: D003920:Diabetes Mellitus / Q000453:epidemiology / Q000209:etiology*; D005544:Forecasting; D006801:Humans; D015994:Incidence; D007198:Indians, North American; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012307:Risk Factors