One-class Classification for heart disease diagnosis

As has been shown by the recent literature, machine learning techniques are important tools for diagnosing a number of diseases. Hospitals and medical clinics store a large amount of data with respect to the treatment of their patients. However, rarely an analysis of these data is conducted in order to extract intrinsic information for modeling a specific problem. This work presents an analysis of medical data aimed at determining whether or not patients are cardiac. To this end, raw data was collected and preprocessed at a Brazilian local hospital in order to build a new dataset containing only non-invasive information of children with heart murmur symptoms. The gathered data contain information, such as height, weight, gender and birthday date. The collected data was shown to be very imbalanced. Due to this imbalance, we employ the One-class Classification (OCC) paradigm to solve the problem by experimenting five methods; including the FBDOCC, that we proposed in a previous paper. Furthermore, two additional datasets were experimented in order to assess effectiveness of One-Class classifiers on the domain of heart disease detection. The overall results show that the FBDOCC succeeded in this task, yielding, statistically, the best performance for the gathered dataset as well as the other two heart disease datasets.