Artificial neural networks can classify uninvestigated patients with dyspepsia.

There is consensus on investigating older patients presenting with or without alarm symptoms and/or risk factors, and irrespective of their Helicobacter pylori status. Remaining patients with uninvestigated dyspepsia, however, represents a 'grey' population for whom no clearly defined guidelines have been delineated. Physicians often struggle with the decision of whether or not to undertake noninvasive testing, treat dyspeptic patients empirically or perform an invasive endoscopy of the upper gastrointestinal tract. We have explored the contribution of artificial neural networks (ANNs) to provide appropriate interpretation of presenting complaints and clinical characteristics for these patients. By taking into account all the 86 recorded features of 101 dyspeptic patients, the overall predictive capability of ANNs in sorting out organic from functional disease amounted to 74.2% and increased to a figure of 85.0% when only the 55 best performing input variables were analyzed. The ANNs performed much better in extracting those patients with a functional dyspepsia (90% accuracy rate), but even in patients with organic disease the 80% accuracy value was remarkable. In patients with an uninvestigated dyspepsia, ANNs found a unique combination of socioenvironmental data, past medical history, risk factors for organic disease, and presenting abdominal complaints that each patient brings to the clinical encounter. With this ability, ANNs can be used to assist in the classification and treatment of patients with uninvestigated dyspepsia, and to bring a greater level of confidence to this process.