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.

当前的在线健康讨论论坛通过社交博客上的健康博客，帖子，推文和聊天记录，生成了大量的数字化数据。人们发布查询，经历的健康问题以及症状，诊断和临床报告，以指导预防措施和医疗救助。作为案例研究，这项工作着重于检测多囊卵巢综合症，这是一种影响女性激素水平的普遍状况。已经对这一PCOS问题进行了调查，因为它构成了不孕症，心脏病，糖尿病，中风和许多此类疾病的高危因素。我们提出了一种名为DEODORANT的新型模型（使用关联规则的炒作和主导集的正确性来检测和诊断多囊卵巢综合征），以推导出实时采矿数据的预期用途。使用NLTK对从各种媒体源收集的非结构化数据进行预处理，并通过应用先验算法得出关联规则。这些关联规则在超图中表示，然后重新生成为折线图，以使其适合于聚类构建。对线图执行谱聚类以将其划分为超图簇。通过在结果超图上应用支配集属性，可以得出所需的推论。从实验结果来看，从每个聚类的主导集得出的结果的支持值已显示出症状和成因，并按百分比排序。显然，它们与描绘真实统计数据的精确结果保持一致。这种类型的分析将使医生和卫生组织能够跟踪疾病，