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According with the World Health Organization, around 50 million people in the world have epilepsy. After the diagnosis process, physicians classify epilepsy according to the International Classification of Diseases, Ninth Revision (ICD-9). Often exams as electroencephalograms and magnetic resonances are used to create a more accurate diagnosis in a short amount of time. The classification process is time consuming and demands the realization of complementary exams. To circumvent this laborious process we propose an automatic process of classifying epileptic diagnoses based on ICD-9. We put forward a text mining approach, using processed electronic medical records and a K-Nearest Neighbor is applied as a white-box multi classifier approach to classify each instance mapping into the corresponding standard code.Results suggests a good performance proposing a diagnosis from electronic medical records, despite of the reduced volume of available training data

ICD9-based Text Mining Approach to Children Epilepsy Classification