An Evolutionary Approach To Cascade Multiple Classifiers A Case-Study To Analyze Textual Content Of Medical Records And Identify Potential Diagnosis

Abstract This paper describes an experiment where classifiers are used to identify potential diagnoses on examining textual content of medical records. Three classifiers are applied separately k-nearest neighborhood multilayer perceptron and support vector machines and also combined in two different approaches parallel and cascading results show that even accuracy point to a best alternative ROC analysis show that choosing an approach depends on an acceptable error level.