Non-Communicable Diseases Classification using Multi-Label Learning Techniques

Non-communicable diseases: NCDs are one of leading causes of death in the world. Multi-NCDs patients tend to undergo and suffer from multiple coexistent diseases. This research aims at classifying NCDs patients who are diagnosed with other NCDs. Multi-label classification was used in this research. There are four diseases types used in this study, i.e. diabetes, hyper-tension, cardiovascular and stroke. Binary relevance (BR), Classifier Chains (CC), The random k-Iabelsets (RAkEL) and Multi-Label k-Nearest Neighbor (ML-KNN) are adopted to transform Multi-NCDs to disease label. The experiments are conducted on the physical examination datasets collected from electronic health records. In the experiments, the comparative results of the techniques are demonstrated. The result showed that the RAkEL method outperformed other methods and achieved the best accuracy of 91.07%.