skip to main content
10.1145/3372422.3372451acmotherconferencesArticle/Chapter ViewAbstractPublication PagesciisConference Proceedingsconference-collections
research-article

Data Mining in Health Care Sector: Literature Notes

Authors Info & Claims
Published:07 February 2020Publication History

ABSTRACT

A standout amongst the most essential strides of the knowledge discovery in database KDD is data mining. Data mining is defined as a basic advance during the time spent learning discovery in databases in which understanding strategies are utilized in order to pattern discovery. Due to the huge amount of data available within the healthcare systems, data mining is important for the healthcare sector in the clinical and diagnosis diseases. However, data mining and healthcare organizations have developed some of dependable early discovery frameworks and different healthcare related frameworks from the clinical treatment and analysis information. The main motivation of this paper is to give a survey of data extraction in health care. In addition, the benefits and obstacles of the use of data extraction strategies in health care and therapeutic information have been thought.

References

  1. American Medical Informatics Association, http://www.amia.org/informatics/.Google ScholarGoogle Scholar
  2. Kaur, H., & Wasan, S. K. 2006. Empirical study on applications of data mining techniques in healthcare. Journal of Computer science, 2, 2(2006), 194--200.Google ScholarGoogle ScholarCross RefCross Ref
  3. Daliri, Z. S. 2017. Data Mining for Health Care Industry: A Practical Machine Learning Tool. International Research Journal of Multidisciplinary Studies, 3(4).Google ScholarGoogle Scholar
  4. I. O. Ogundele, O. L. Popoola, O. O. Oyesola, K. T. Orija. 2018. A Review on Data Mining in Healthcare. In International Journal of Advanced Research in Computer Engineering and Technology (IJARCET). September 2018.Google ScholarGoogle Scholar
  5. Pandey, S. C. 2016. Data mining techniques for medical data: a review. In 2016 International Conference on Signal Processing, Communication, Power and Embedded System. 972--982. IEEE.Google ScholarGoogle Scholar
  6. Q. Yang, X. Wu. 2006. 10 Challenging problems in data mining research. In International Journal of Information Technology and Decision Making. 5, 4 (2006), 597604.Google ScholarGoogle Scholar
  7. K. Cios, G. W. Moore. 2002. Uniqueness of Medical Data Mining. In Artificial Intelligence in Medicine.Google ScholarGoogle Scholar
  8. V. krishnaiah, G. Narsimha and N. C. Subhash. 2013. A study on clinical prediction using Data Mining techniques. In International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) 3, 1 (March 2013), 239--248.Google ScholarGoogle Scholar
  9. Alfarraj, Osama, and Ahed Abugabah. 2017. Extending information system models to the health care context: an empirical study and experience from developing countries. In Int. Arab J. Inf. Technology. 14, 2(2017), 159--167.Google ScholarGoogle Scholar
  10. Al Smadi, Ahmad and Abugabah, Ahed (2017). Intelligent Information Systems and Image Processing: A Novel Pan-Sharpening Technique Based on Multiscale DecompositionGoogle ScholarGoogle Scholar
  11. In the Proceedings of the 2018 the 2nd International Conference on Video and Image,.Google ScholarGoogle Scholar
  12. Sharma, Sugam. 2019. Concept of Association Rule of Data Mining Assists Mitigating the Increasing Obesity. In Healthcare Policy and Reform: Concepts, Methodologies, Tools, and Applications. (2019), 518--536.Google ScholarGoogle Scholar
  13. Tambe, B. Sagar, S. G. Suhas. 2018. Cluster-based real-time analysis of mobile healthcare application for prediction of physiological data. In Journal of Ambient Intelligence and Humanized Computing. 9, 2(2018), 429--445.Google ScholarGoogle ScholarCross RefCross Ref
  14. S. Bharati, M. A. Rahman, P. Podder. 2018. Breast Cancer Prediction Applying Different Classification Algorithm with Comparative Analysis using WEKA. In 4th International Conference on Electrical Engineering and Information and Communication Technology. (Sep 2018), 581584.Google ScholarGoogle ScholarCross RefCross Ref
  15. H. D. Masethe and M. A. Masethe. 2014. Prediction of heart disease using classification algorithms. In Proceedings of the world Congress on Engineering and computer Science. 2 (2014), 2224.Google ScholarGoogle Scholar
  16. A. Chaudhary, P. Garg. 2014. Detecting and Diagnosing a Disease by Patient Monitoring System. In International Journal of Mechanical Engineering and Information Technology. 2, 6 (2014), 493--99.Google ScholarGoogle Scholar
  17. A. Bartschat, M. Reischl, R. Mikut. 2018. Data mining tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.Google ScholarGoogle Scholar
  18. Rau, Ionu. 2016. Data mining in healthcare: decision maing and precision. In Database Systems Journal. 6, 4(2016), 33--40.Google ScholarGoogle Scholar
  19. N. Sharma and H. Om. 2013. Data mining models for predicting oral cancer survivability, in Netw. Model. Anal. Heal. In Informatics Bioinformation. 2, 4 (2013), 28529.Google ScholarGoogle Scholar
  20. Chaurasia, Vikas, P. Saurabh, and B. B. Tiwari. 2018. Prediction of benign and malignant breast cancer using data mining techniques. In Journal of Algorithms and Computational Technology. 12, 2(2018), 119--126.Google ScholarGoogle ScholarCross RefCross Ref
  21. A. Sanjay, H. V. Nair, S. Murali, K. S. Krishnaeni. 2018. A Data Mining Model to Predict Breast Cancer Using Improved Feature Selection Method on Real Time Data. In International Conference on Advances in Computing, Communications and Informatics. (Sep 2018), 24372440.Google ScholarGoogle ScholarCross RefCross Ref
  22. M. Pradhan. 2018. Indian Healthcare Service Management Through Data Mining: Datamining for Healthcare Services. In Next-Generation Mobile and Pervasive Healthcare Solutions IGI Global. (2018) 219--233.Google ScholarGoogle Scholar
  23. H. C. Koh and G. Tan. 2005. Data Mining Application in Healthcare. In Journal of Healthcare Information Management. 19, 2(2005).Google ScholarGoogle Scholar
  24. Fiumara, Giacomo, et al. 2018. Applying Artificial Intelligence in Healthcare Social Networks to Identity Critical Issues in Patients' Posts. In HEALTHINF.Google ScholarGoogle Scholar
  25. Soni, Sunita. 2016. Overview of Predictive Modeling Approaches in Health Care Data Mining. In Business Intelligence: Concepts, Methodologies, Tools, and Applications. (2016)73--95.Google ScholarGoogle Scholar
  26. Durairaj, Manoj, and R. Veera. 2013. Data mining applications in healthcare sector: a stud. In International journal of scientific and technology research. 2, 10(2013) 29--35.Google ScholarGoogle Scholar
  27. Sharma, Sugam. 2019. Concept of Association Rule of Data Mining Assists Mitigating the Increasing Obesity. In Healthcare Policy and Reform: Concepts, Methodologies, Tools, and Applications, IGI Global. (2019) 518536.Google ScholarGoogle Scholar
  28. Ludwig, S. A., S. Picek, D. Jakobovic. 2018. Classification of cancer data: analyzing gene expression data using a fuzzy decision tree algorithm. In Operations Research Applications in Health Care Management, Springer, Cham. (2018), 327--347.Google ScholarGoogle Scholar
  29. Saraiya, R. Parth, and G. Yogita. 2018. Study of Clustering Techniques in the Data Mining Domain. In International Journal of Computer Science and Mobile Computing - IJCSMC. 7, 11 (Nov 2018), 3137.Google ScholarGoogle Scholar
  30. Getoor, Lise, and Christopher P. Diehl. 2005. Link mining: a survey. In Acm Sigkdd Explorations Newsletter. 7, 2 (2005), 3--12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Zhen, Xiantong, et al. 2018. Multi-target regression via robust low-rank learning," in IEEE transactions on pattern analysis and machine intelligence. 40, 2 (2018), 497--504.Google ScholarGoogle Scholar
  32. Ahmed, Mohiuddin. 2019. Data summarization: a survey. In Knowledge and Information Systems. 58, 2 (2019), 249--273.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. J. J. Yang, J. Li, J. Mulder, Y. Wang, S. Chen, H. Wu, Q. Wang, and H. Pan. 2015. Emerging information technologies for enhanced healthcare. In Comput. Ind.69(2015) 311.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. N. Wickramasinghe, S. K. Sharma, and J. N. D. Gupta. 2005. Knowledge Management in Healthcare. 63(2005), 518.Google ScholarGoogle Scholar
  35. B. Liu, Y. Xiao, L. Cao, Z. Hao, and F. Deng. 2013. SVDD-based outlier detection on uncertain data. In Knowl. Inf. Syst. 34, 3 (2013), 597--618.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. R. Veloso et al. 2014. A clustering approach for predicting readmissions in intensive medicine. In Procedia Technol. 16 (2014), 1307--1316.Google ScholarGoogle ScholarCross RefCross Ref
  37. R. Veloso, F. Portela, M. F. Santos, Silva, F. Rua, A. Abelha, and J. Machado. 2014. A Clustering Approach for Predicting Readmissions in Intensive Medicine. In Procedia Technol. 16 (2014), 1307--1316.Google ScholarGoogle ScholarCross RefCross Ref
  38. Berkhin, Pavel. 2006. A survey of clustering data mining techniques. In Grouping multidimensional data. Springer, Berlin, Heidelberg. (2006), 2571.Google ScholarGoogle Scholar
  39. Khamis, S. Hassan, W. C. Kipruto, and K. Stephen. 2014. Application of knearest neighbour classification in medical data mining. In International Journal of Information and Communication Technology Research. 4, 4 (2014).Google ScholarGoogle Scholar
  40. Deekshatulu, B. L., and C. Priti. 2013. Classification of heart disease using k-nearest neighbor and genetic algorithm. In Procedia technology. 10 (2013), 85--94.Google ScholarGoogle Scholar
  41. Aich, S., Younga, K., Hui, K. L., Al-Absi, M. Sain. 2018. A nonlinear decision tree-based classification approach to predict the Parkinson's disease using different feature sets of voice data. In 20th International Conference on Advanced Communication Technology. (Feb 2018), 638--642.Google ScholarGoogle Scholar
  42. A. Linden, P. R. Yarnold. 2018. Identifying causal mechanisms in health care interventions using classification tree analysis. In Journal of evaluation in clinical practice. 24, 2 (April 2018), 353--61.Google ScholarGoogle ScholarCross RefCross Ref
  43. A. Malav, K. Kadam and P. Kamat. 2017. Prediction of heart disease using k-means and artificial neural network as Hybrid Approach to Improve Accuracy. In International Journal of Engineering and Technology. 9, 4 (2017), 3081--3085.Google ScholarGoogle ScholarCross RefCross Ref
  44. A. Malav, K. Kadam. 2018. A Hybrid Approach for Heart Disease Prediction Using Artificial Neural Network and K-means. In International Journal of Pure and Applied Mathematics. 118, 8 (2018), 103--10.Google ScholarGoogle Scholar
  45. S. A. Pattekari, and A. Parveen. 2012. Prediction system for heart disease using Nave Bayes. In International Journal of Advanced Computer and Mathematical Sciences. 3, 3 (2012), 290--294.Google ScholarGoogle Scholar
  46. P. Lucas. 2004. Bayesian analysis, pattern analysis, and data mining in health care. In Current opinion in critical care. 10, 5 (2004), 399--403.Google ScholarGoogle ScholarCross RefCross Ref
  47. Razzaghi, Talayeh, et al. 2016. Multilevel weighted support vector machine for classification on healthcare data with missing values. In PloS one. 11, 5 (2016), e0155119.Google ScholarGoogle ScholarCross RefCross Ref
  48. Y. J. Son, H. G. Kim, E. H. Kim, S. Choi and S. K. Lee. 2010. Application of support vector machine for prediction of medication adherence in heart failure patients. In Healthcare informatics research. 16, 4 (2010), 253--259.Google ScholarGoogle Scholar

Index Terms

  1. Data Mining in Health Care Sector: Literature Notes

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        CIIS '19: Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems
        November 2019
        200 pages
        ISBN:9781450372596
        DOI:10.1145/3372422

        Copyright © 2019 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 7 February 2020

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited
      • Article Metrics

        • Downloads (Last 12 months)32
        • Downloads (Last 6 weeks)7

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader