International Journal of Medical Informatics 57 (2000) 41 – 55 Improving diagnostic accuracy using a hierarchical neural network to model decision subtasks David West a, *, Vivian West b a Department of Decision Sciences, College of Business Administration, East Carolina Uniersity, Greenille, NC 27836, USA b East Carolina Uniersity Center for Health Sciences Communication, Greenille, NC 27836, USA and School of Nursing, Uniersity of North Carolina, Chapel Hill, NC 27599, USA Received 26 March 1999; received in revised form 30 September 1999; accepted 30 September 1999 Abstract A number of quantitative models including linear discriminant analysis, logistic regression, k nearest neighbor, kernel density, recursive partitioning, and neural networks are being used in medical diagnostic support systems to assist human decision-makers in disease diagnosis. This research investigates the decision accuracy of neural network models for the differential diagnosis of six erythamatous-squamous diseases. Conditions where a hierarchical neural network model can increase diagnostic accuracy by partitioning the decision domain into subtasks that are easier to learn are specifically addressed. Self-organizing maps (SOM) are used to portray the 34 feature variables in a two dimensional plot that maintains topological ordering. The SOM identifies five inconsistent cases that are likely sources of error for the quantitative decision models; the lower bound for the diagnostic decision error based on five errors is 0.0140. The traditional application of the quantitative models cited above results in diagnostic error levels substantially greater than this target level. A two-stage hierarchical neural network is designed by combining a multilayer perceptron first stage and a mixture-of-experts second stage. The second stage mixture-of-experts neural network learns a subtask of the diagnostic decision, the discrimination between seborrheic dermatitis and pityriasis rosea. The diagnostic accuracy of the two stage neural network approaches the target performance established from the SOM with an error rate of 0.0159. © 2000 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Computer-aided diagnosis; Diagnostic accuracy; Learning subtasks; Mixture-of-experts neural network; Self-organizing map www.elsevier.com/locate/ijmedinf 1. Introduction Medical diagnostic decision support sys- tems (MDSS) have become an established component of medical technology and their use will continue to grow, fueled by electronic medical record and automatic data capture [27]. The purpose of a MDSS is to augment, not replace the natural capabilities of human diagnosticians in the complex process of med- ical diagnosis. To date, the most successful MDSS applications are in limited, focused * Corresponding author. Tel.: +1-252-328-6370. E-mail address: westd@mail.ecu.edu (D. West) 1386-5056/00/$ - see front matter © 2000 Elsevier Science Ireland Ltd. All rights reserved. PII:S1386-5056(99)00059-3