Risk-factor fusion for predicting multifactorial diseases

A generalized classification methodology is developed to predict the presence or absence of a multifactorial disease from a set of risk factors thought to be correlated with the disease. The methodology includes fusion to combine risk factors into a single feature vector, normalization to overcome the problems associated with fusing features which have different formats and ranges, discrete Karhunen-Loeve transform (DKLT)-based transformation to facilitate parametric classifier development, the selection of features with high interclass separations, and the design of parametric classifiers. The validity of the method is demonstrated by applying it to predict the occurrence of gout from 14 risk factors. Cross-validation evaluations on 96 patients, 48 clinically diagnosed to have gout and 48 diagnosed to not have gout, showed that an average classification accuracy of 75.7% can be obtained. Even more promising is that higher classification accuracies can be achieved through the careful selection of the DKLT transformation matrix which in turn involves selecting design sets that are good representatives of the gout and nongout classes. It is concluded that the generalized methodology developed in this paper is quite effective in predicting multifactorial diseases and can, therefore, assist/support a physician in diagnosing a multifactorial disease.