Knowledge discovery in clinical databases with neural network evidence combination

Diagnosis of diseases and disorders afflicting mankind has always been a candidate for automation. Numerous attempts made at classification of symptoms and characteristic features of disorders have rarely used neural networks due to the inherent difficulty of training with sufficient data. But, the inherent robustness of neural networks and their adaptability in varying relationships of input and output justifies their use in clinical databases. To overcome the problem of training under conditions of insufficient and incomplete data, we propose to use three different neural network classifiers, each using a different learning function. Consequent combination of their beliefs by Dempster-Shafer evidence combination overcomes weaknesses exhibited by any one classifier to a particular training set. We prove with conclusive evidence that such an approach would provide a significantly higher accuracy in the diagnosis of disorders and diseases.