Data mining and ontology-based techniques in healthcare management

Recently, large amounts of data have been produced due to the achieved advances in biotechnology and health sciences fields. It includes clinical information and genetic data which contained in electronic health records (EHRs). Therefore, there was a need for innovative and effective methods for representing this amount of data. On the other side, it is very important to detect syndromes, which can badly influence the human health in addition to putting financial burdens on their shoulders, in an early stage to avoid many complications. Recently, different data mining techniques in addition to ontology-based techniques have played a great role in building automated systems that have the ability to detect syndromes efficiently and accurately. In this paper, we cover some of the research efforts that have employed either the data mining techniques or ontology-based techniques, or both in detecting syndromes. Additionally, a set of well-known data mining techniques including decision trees (J48), Naive Bayes, multi-layer perceptron (MLP), and random forest (RF) has been assessed in performing the classification task using a publicly available heart diseases dataset.