A system for providing computer-aided analysis of medical images comprising: an image processor for processing medical image data; a decision engine for generating a diagnosis based on the processed medical image data alone and further computing possible diagnostic outcomes based on different proposed possible values for clinical data; a database of prior diagnoses, their accompanying probabilities, and classifier algorithms for assessing probability of an illness given image data alone, image data with incomplete clinical data, and/or image data with clinical data; an interface engine for requesting and entering clinical data; and a display terminal for displaying the results of the computer-aided analysis, wherein the system is further adapted to develop ensembles of classifiers for use in the computer aided analysis, the developing comprising making available a set of patient cases, each with multiple data types, as a training database for training, inducing a decision tree on the clinical data as a first level classifier to roughly classify the patients based on a final outcome, using the decision tree to stratify the patients in the training data base into high risk or low risk of having a particular illness, developing a set of possible classifiers based on the clinical data, ignoring any stratification of the patients, performing a test for each of the classifiers for performance on the training data, the classifiers with high performance on each strata of patients being kept in separate groups such that the result for y strata of the patients includes y sets of the classifiers, placing the z best performing classifiers on each strata or all classifiers with a minimum required performance of accuracy, sensitivity, or specificity, in the corresponding classifier set, the set of classifiers for each strata forming a classifier ensemble, and storing the clinical decision tree and separate classifier ensembles as output. an image processor for processing medical image data; a decision engine for generating a diagnosis based on the processed medical image data alone and further computing possible diagnostic outcomes based on different proposed possible values for clinical data; a database of prior diagnoses, their accompanying probabilities, and classifier algorithms for assessing probability of an illness given image data alone, image data with incomplete clinical data, and/or image data with clinical data; an interface engine for requesting and entering clinical data; and a display terminal for displaying the results of the computer-aided analysis, wherein the system is further adapted to develop ensembles of classifiers for use in the computer aided analysis, the developing comprising making available a set of patient cases, each with multiple data types, as a training database for training, inducing a decision tree on the clinical data as a first level classifier to roughly classify the patients based on a final outcome, using the decision tree to stratify the patients in the training data base into high risk or low risk of having a particular illness, developing a set of possible classifiers based on the clinical data, ignoring any stratification of the patients, performing a test for each of the classifiers for performance on the training data, the classifiers with high performance on each strata of patients being kept in separate groups such that the result for y strata of the patients includes y sets of the classifiers, placing the z best performing classifiers on each strata or all classifiers with a minimum required performance of accuracy, sensitivity, or specificity, in the corresponding classifier set, the set of classifiers for each strata forming a classifier ensemble, and storing the clinical decision tree and separate classifier ensembles as output. making available a set of patient cases, each with multiple data types, as a training database for training, inducing a decision tree on the clinical data as a first level classifier to roughly classify the patients based on a final outcome, using the decision tree to stratify the patients in the training data base into high risk or low risk of having a particular illness, developing a set of possible classifiers based on the clinical data, ignoring any stratification of the patients, performing a test for each of the classifiers for performance on the training data, the classifiers with high performance on each strata of patients being kept in separate groups such that the result for y strata of the patients includes y sets of the classifiers, placing the z best performing classifiers on each strata or all classifiers with a minimum required performance of accuracy, sensitivity, or specificity, in the corresponding classifier set, the set of classifiers for each strata forming a classifier ensemble, and storing the clinical decision tree and separate classifier ensembles as output.