Preprocessing unbalanced data using weighted support vector machines for prediction of heart disease in children

Machine learning techniques are an important tool for diagnosing a number of diseases, as has been shown by the recent literature. Hospitals and medical clinics have a huge amount of data about the treatment of their patients, however, rarely analysis of these data is performed in order to extract intrinsic information aimed at modeling a specific problem. This work presents an analysis of medical data aimed at determining whether children patients are cardiac or not. To this end, raw data was collected at a Brazilian local hospital to be preprocessed in order to build the classification models. Only non invasive information were used, such as height, weight, gender and birthday date to create another set of derived variables such as BMI (Body Mass Index) to support the classification phase. However, the collected data was shown to be very imbalanced. Aimed at treat this problem, many tecniques were employed and one new approach was proposed. The results shown that the proposed approach outperforms the other methods in three out of four evaluation metrics.