DEODORANT: a novel approach for early detection and prevention of polycystic ovary syndrome using association rule in hypergraph with the dominating set property

Present online health discussion forums generate the bountiful amount of digitized data through health-blogs, posts, tweets, and chats in the social media. People post queries, health issues they undergo along with the symptoms, diagnosis, and clinical reports to get direction for preventive measures and medical relief. As a case study, this work focus on detection of Polycystic Ovary Syndrome, a prevalent condition that affects a woman’s hormone levels. This PCOS problem has been investigated as it forms high-risk factor for infertility, heart disease, diabetes, stroke and many such diseases. We propose a novel model named as DEODORANT (Detection and prEvention of polycystic Ovary synDrome using assOciation rule hypeRgrAph and domiNating set properTy) to derive prospective use of real-time mining data. The unstructured data collected from various media sources are preprocessed using NLTK and association rules are derived by applying apriori algorithm. These association rules are represented in hypergraph and then regenerated as line graph to make it suitable for cluster construction. Spectral clustering is performed on line graph to partition into clusters of hypergraphs. By applying dominating set property on the resultant hypergraph, required inferences can be elicited. From the experimental results, support value of the outcome derived from the dominating set of each cluster has exhibited the symptoms and the causes with percentage ranking. It is evident that they get aligned with precise result portraying real statistics. This type of analysis will empower doctors and health organizations to keep track of the diseases, their symptoms for early detection and safe recovery.