Constructing classification rules for accurate diagnosis of a disorder is an important goal in medical practice. In many clinical applications, there is no clinically significant anatomical or physiological deviation exists to identify the gold standard disease status to inform development of classification algorithms. Despite absence of perfect disease class identifiers, there are usually one or more disease-informative auxiliary markers along with feature variables comprising known symptoms. Existing statistical learning approaches do not effectively draw information from auxiliary prognostic markers. We propose a large margin classification method, with particular emphasis on the support vector machine (SVM), assisted by available informative markers in order to classify disease without knowing a subject's true disease status. We view this task as statistical learning in the presence of missing data, and introduce a pseudo-EM algorithm to the classification. A major distinction with a regular EM algorithm is that we do not model the distribution of missing data given the observed feature variables either parametrically or semiparametrically. We also propose a sparse variable selection method embedded in the pseudo-EM algorithm. Theoretical examination shows that the proposed classification rule is Fisher consistent, and that under a linear rule, the proposed selection has an oracle variable selection property and the estimated coefficients are asymptotically normal. We apply the methods to build decision rules for including subjects in clinical trials of a new psychiatric disorder and present four applications to data available at the UCI Machine Learning Repository.
Keywords: Classification rules; Diagnostic and Statistical Manual of Mental Disorders; Large margin classification; Missing data; Statistical learning; Support vector machine.