A MAHALANOBIS DISTANCE BASED CLASSIFIER FOR DIAGNOSIS OF DISEASES

Abstract

ABSTRACT Multi-dimensional information, including many different biochemical testing items, is conventionally used to characterize medical examination data. However, ambiguous and inconsistent multi-dimensional data are difficult to explore the relationship among them. The conventional means of developing an expert system requires formulating rules to analyze the input data. Formulating such rules is quite difficult with large sets of input data. This study presents a classification approach for diagnosing medical examination information. This is a multivariate analysis approach based on Mahalanobis distance (MD) classifier, creates the MD space in advance using the homogenous examination data. Then, an automatic thresholding technique adopted as a decision criterion in a MD space, is based on the maximum variance between-classes. Comparing the MD classification approach with the neural network approach reveals that the MD classifier is highly effective in diagnosing liver diseases because it has a classification accuracy exceeding 95%. In addition, the MD classifier outperforms neural network classifier by increasing the correct classification up to 5.97% for the testing sets. Finally, some characteristics are discussed.