Using neural networks to predict the onset of diabetes mellitus.
Classification is an important decision making tool, especially in the medical sciences. Unfortunately, while several classification procedures exist, many of the current methods fail to provide adequate results. In recent years, artificial neural networks have been suggested as an alternative tool for classification. Here, we use neural networks to predict the onset of diabetes mellitus in Pima Indian women. The modeling capabilities of neural networks are compared to traditional methods like logistic regression and to a specific method called ADAP, which has been used to predict diabetes. The results indicate that neural networks are indeed a viable approach to classification. Furthermore, we attempt to provide a basis upon which neural networks can be used for variable selection in statistical modeling.
Shanker
M S
MS
Department of Administrative Sciences, Kent State University, Ohio 44242, USA. mshanker@scorpio.kent.edu
D002965:Classification; D003924:Diabetes Mellitus, Type 2 / Q000209:etiology*; D005260:Female; D006801:Humans; D007198:Indians, North American; D016015:Logistic Models; D016571:Neural Networks, Computer; D011247:Pregnancy; D012307:Risk Factors