Abstract
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.
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References
Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: SIGMOD '93: proceedings of the 1993 ACM SIGMOD international conference on management of data. https://doi.org/10.1145/170035.170072
Anandhavalli M, Ghose MK, Gauthaman K (2010) Association rule mining in genomics. Int J Comput Theory Eng 2(2):269
Atluri G, Gupta R, Fang G, Pandey G, Steinbach M, Kumar V (2009) Association analysis techniques for bioinformatics problems. International Conference on Bioinformatics and Computational Biology. Springer, Berlin, Heidelberg, pp 1–13
Berge C (1984) Hypergraphs: combinatorics of finite sets, vol 45. Elsevier, Amsterdam
Berkhin P (2006) A survey of clustering data mining techniques. Grouping multidimensional data. Springer, Berlin, Heidelberg, pp 25–71
Bretto A (2013) Applications of hypergraph theory: a brief overview. Hypergraph theory. Springer, Heidelberg, pp 111–116
Brijs T, Vanhoof K, Wets G (2003) Defining interestigness for association rules institute of information theories and applications. FOI ITHEA 10(4). http://hdl.handle.net/10525/964
Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. In: SIGMOD '93: proceedings of the 1993 ACM SIGMOD international conference on management of data, pp 207–216. https://doi.org/10.1145/170035.170072
Bujtás C, Henning MA, Tuza Z (2012) Transversals and domination in uniform hypergraphs. Eur J Comb 33(1):62–71
Chung K, Yoo H, Choe DE (2018) Ambient context-based modeling for health risk assessment using deep neural network. J Ambient Intell Human Comput 2018:1–9
Derbas N, Dusserre E, Padró M, Segond F (2020) Eventfully safapp: hybrid approach to event detection for social media mining. J Ambient Intell Human Comput 11:87–95. https://doi.org/10.1007/s12652-018-1078-7
Dunham MH, Xiao Y, Gruenwald L, Hossain Z (2000) A survey of association rules. ACM Surv J. http://www2.cs.uh.edu/~ceick/6340/grue-assoc.pdf
Edara DC, Vanukuri LP, Sistla V, Kolli VKK (2019) Sentiment analysis and text categorization of cancer medical records with LSTM. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01399-8
Erlandsson F, Bródka P, Borg A, Johnson H (2016) Finding influential users in social media using association rule learning. Entropy 18(5):164
Han EH, Karypis G, Kumar V, Mobasher B (1998) Hypergraph based clustering in high-dimensional data sets: a summary of results. IEEE Data Eng Bull 21(1):15–22. https://www.researchgate.net/profile/Vipin_Kumar26/publication/220283323_Hypergraph_Based_Clustering_in_High-Dimensional_Data_Sets_A_Summary_of_Results/links/004635161f313eecc2000000.pdf
Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam
Hotho A, Nürnberger A, Paaß G (2005) A brief survey of text mining. In: Ldv forum. https://www.researchgate.net/publication/215514577
Jain VK, Kumar S (2018) Effective surveillance and predictive mapping of mosquito-borne diseases using social media. J Comput Sci 25:406–415
Janani MR, Vijayarani S (2019) Text document clustering using spectral clustering algorithm with particle swarm optimization. Exp Syst Appl. https://doi.org/10.1016/j.eswa.2019.05.030
Kaur M, Kang S (2016) Market basket analysis: identify the changing trends of market data using association rule mining. Proc Comput Sci 85:78–85
Li X et al (2014) Context-aware hypergraph construction for robust spectral clustering. IEEE Trans Knowl Data Eng 26(10):2588–2597
Lopes ADA, Pinho R, Paulovich FV, Minghim R (2007) Visual text mining using association rules. Comput Graph 31(3):316–326
Manimaran J, Velmurugan (2014) A survey of association rule mining in text applications. In: 2013 IEEE international conference on computational intelligence and computing research. IEEE, pp 1–5. https://doi.org/10.1109/ICCIC.2013.6724258
Mijangos V, Sierra G, Montes A (2017) Sentence level matrix representation for document spectral clustering. Pattern Recogn Lett 85:29–34
Mofizur Rahman C, Sohel FA, Naushad P, Kamruzzaman SM (2010) Text classification using the concept of association rule of data mining. In: Proceedings international conference on information technology, Kathmandu, Nepal, pp 234–241. arXiv:1009.4582v1
Molnár B (2014) Applications of hypergraphs in informatics: a survey and opportunities for research. Ann Univ Sci Budapest Sect Comput 42:261–282
Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. In: Advances in neural information processing systems, pp 849–856. http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf
Prajapati DJ, Garg S, Chauhan NC (2017) Interesting association rule mining with consistent and inconsistent rule detection from big sales data in distributed environment. Future Comput Inf J 2(1):19–30
Purkait P, Chin TJ, Sadri A, Suter D (2017) Clustering with hypergraphs: the case for large hyperedges. IEEE Trans Pattern Anal Mach Intell 39(9):1697–1711
Shah AM, Yan X, Shah SAA, Mamirkulova G (2019) Mining patient opinion to evaluate the service quality in healthcare: a deep-learning approach. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01434-8
Shao Z, Li Y, Wang X, Zhao X, Guo Y (2020) Research on a new automatic generation algorithm of concept map based on text analysis and association rules mining. J Ambient Intell Human Comput 11:539–551. https://doi.org/10.1007/s12652-018-0934-9
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905
Valdivia A, Martínez-Cámara E, Chaturvedi I, Luzón MV, Cambria E, Ong YS, Herrera F (2020) What do people think about this monument? Understanding negative reviews via deep learning, clustering and descriptive rules. J Ambient Intell Human Comput 11(1):39–52
Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416
Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H et al (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37
Yu X, Liu H, Shi J, Hwang JN, Wan W, Lu J (2014) Association rule mining of personal hobbies in social networks. In: 2014 IEEE international congress on big data, Anchorage, AK, USA. https://doi.org/10.1109/BigData.Congress.2014.52
Zhang J, Chen M, Hu E, Wu L (2020) Data mining model for food safety incidents based on structural analysis and semantic similarity. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01750-4
Zhou D, Huang J, Schölkopf B (2007) Learning with hypergraphs: Clustering, classification, and embedding. In: Advances in neural information processing systems, pp 1601–1608. http://papers.nips.cc/paper/3128-learning-with-hypergraphs-clustering-classification-and-embedding.pdf
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The authors of the paper gratefully acknowledge the “TaTa—Realty Srinivasa Ramanujan Research cell of our University for the financial support extended to us in carrying out this research work”.
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Pradeepa, S., Geetha, K., Kannan, K. et al. DEODORANT: a novel approach for early detection and prevention of polycystic ovary syndrome using association rule in hypergraph with the dominating set property. J Ambient Intell Human Comput 14, 5421–5437 (2023). https://doi.org/10.1007/s12652-020-01990-4
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DOI: https://doi.org/10.1007/s12652-020-01990-4