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Techniques of Data Mining In Healthcare: A Review

by Parvez Ahmad, Saqib Qamar, Syed Qasim Afser Rizvi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 15
Year of Publication: 2015
Authors: Parvez Ahmad, Saqib Qamar, Syed Qasim Afser Rizvi
10.5120/21307-4126

Parvez Ahmad, Saqib Qamar, Syed Qasim Afser Rizvi . Techniques of Data Mining In Healthcare: A Review. International Journal of Computer Applications. 120, 15 ( June 2015), 38-50. DOI=10.5120/21307-4126

@article{ 10.5120/21307-4126,
author = { Parvez Ahmad, Saqib Qamar, Syed Qasim Afser Rizvi },
title = { Techniques of Data Mining In Healthcare: A Review },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 15 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 38-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number15/21307-4126/ },
doi = { 10.5120/21307-4126 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:06:20.906015+05:30
%A Parvez Ahmad
%A Saqib Qamar
%A Syed Qasim Afser Rizvi
%T Techniques of Data Mining In Healthcare: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 15
%P 38-50
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare.

References
  1. D. Hand, H. Mannila and P. Smyth, "Principles of data mining", MIT, (2001).
  2. H. C. Koh and G. Tan, "Data Mining Application in Healthcare", Journal of Healthcare Information Management, vol. 19, no. 2, (2005).
  3. U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, "The KDD process of extracting useful knowledge form volumes of data. commun. ", ACM, vol. 39, no. 11, (1996), pp. 27-34.
  4. J. Han and M. Kamber, "Data mining: concepts and techniques", 2nd ed. The Morgan Kaufmann Series, (2006).
  5. U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, "From data mining to knowledge discovery in databases", Commun. ACM, vol. 39, no. 11, (1996), pp. 24-26.
  6. C. McGregor, C. Christina and J. Andrew, "A process mining driven framework for clinical guideline improvement in critical care", Learning from Medical Data Streams 13th Conference on Artificial Intelligence in Medicine (LEMEDS). http://ceur-ws. org, vol. 765, (2012).
  7. P. R. Harper, "A review and comparison of classification algorithms for medical decision making", Health Policy, vol. 71, (2005), pp. 315-331.
  8. V. S. Stel, S. M. Pluijm, D. J. Deeg, J. H. Smit, L. M. Bouter and P. Lips, "A classification tree for predicting recurrent falling in community-dwelling older persons", J. Am. Geriatr. Soc. , vol. 51, (2003), pp. 1356-1364.
  9. R. Bellazzi and B. Zupan, "Predictive data mining in clinical medicine: current issues and guidelines", Int. J. Med. Inform. , vol. 77, (2008), pp. 81-97.
  10. R. D. Canlas Jr. , "Data Mining in Healthcare:Current Applications and Issues", (2009).
  11. F. Hosseinkhah, H. Ashktorab, R. Veen, M. M. Owrang O. , "Challenges in Data Mining on Medical Databases", IGI Global, (2009), pp. 502-511.
  12. M. Kumari and S. Godara, "Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction", IJCST ISSN: 2229- 4333, vol. 2, no. 2, (2011) June.
  13. J. Soni, U. Ansari, D. Sharma and S. Soni, "Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction", (2011).
  14. C. S. Dangare and S. S. Apte, "Improved Study of Heart Disease Prediction System Using Data Mining Classification Techniques", (2012).
  15. K. Srinivas, B. Kavihta Rani and Dr. A. Govrdhan, "Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks", International Journal on Computer Science and Engineering, vol. 02, no. 02, (2010), pp. 250-255.
  16. A. Aljumah, M. G. Ahamad and M. K. Siddiqui, "Predictive Analysis on Hypertension Treatment Using Data Mining Approach in Saudi Arabia", Intelligent Information Management, vol. 3, (2011), pp. 252-261.
  17. D. Delen, "Analysis of cancer data: a data mining approach", (2009).
  18. O. Osofisan, O. O. Adeyemo, B. A. Sawyerr and O. Eweje, "Prediction of Kidney Failure Using Artificial Neural Networks", (2011).
  19. S. Floyd, "Data Mining Techniques for Prognosis in Pancreatic Cancer", (2007).
  20. M. -J. Huang, M. -Y. Chen and S. -C. Lee, "Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis", Expert Systems with Applications, vol. 32, (2007), pp. 856-867.
  21. S. Gupta, D. Kumar and A. Sharma, "Data Mining Classification Techniques Applied For Breast Cancer Diagnosis And Prognosis", (2011).
  22. K. S. Kavitha, K. V. Ramakrishnan and M. K. Singh, "Modeling and design of evolutionary neural network for heart disease detection", IJCSI International Journal of Computer Science Issues, ISSN (Online): 1694-0814, vol. 7, no. 5, (2010) September, pp. 272-283.
  23. S. H. Ha and S. H. Joo, "A Hybrid Data Mining Method for the Medical Classification of Chest Pain", International Journal of Computer and Information Engineering, vol. 4, no. 1, (2010), pp. 33-38.
  24. R. Parvathi and S. Palaniammalì, "An Improved Medical Diagnosing Technique Using Spatial Association Rules", European Journal of Scientific Research ISSN 1450-216X, vol. 61, no. 1, (2011), pp. 49-59.
  25. S. Chao and F. Wong, "An Incremental Decision Tree Learning Methodology Regarding Attributes in Medical Data Mining", (2009).
  26. Habrard, M. Bernard and F. Jacquenet, "Multi-Relational Data Mining in Medical Databases", Springer-Verlag, (2003).
  27. S. B. Patil and Y. S. Kumaraswamy, "Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network", European Journal of Scientific Research ISSN 1450-216X, © EuroJournals Publishing, Inc. , vol. 31, no. 4, (2009), pp. 642-656.
  28. A. Shukla, R. Tiwari, P. Kaur, Knowledge Based Approach for Diagnosis of Breast Cancer, IEEE International Advance Computing Conference,IACC 2009.
  29. L. Duan, W. N. Street & E. Xu, Healthcare information systems: data mining methods in the creation of a clinical recommender system, Enterprise Information Systems, 5:2, pp169-181 , 2011.
  30. D. S. Kumar, G. Sathyadevi and S. Sivanesh, "Decision Support System for Medical Diagnosis Using Data Mining", (2011).
  31. S. Palaniappan and R. Awang, "Intelligent Heart Disease Prediction System Using Data Mining Techniques", (2008).
  32. Alpaydin, E. (1997), Voting over Multiple Condensed Nearest Neighbors. Artificial Intelligence Review, p. 115–132.
  33. Bramer, M. , (2007) Principles of data mining: Springer.
  34. Goharian & Grossman, Data Mining Classification, Illinois Institute of Technology, http://ir. iit. edu/~nazli/cs422/CS422-Slides/DM-Classification. pdf, (2003).
  35. Apte & S. M. Weiss, Data Mining with Decision Trees and Decision Rules, T. J. Watson Research Center, http://www. research. ibm. com/dar/papers/pdf/fgcsaptewe issue_with_cover. pdf, (1997).
  36. Pagallo, G. and Huassler, D. , Boolean feature discovery in empirical learning, Machine Learning, 5(1): 71-99, 1990.
  37. Quinlan, J. R. , C4. 5: Programs for Machine Learning, Morgan Kaufmann, Los Altos, 1993.
  38. V. Vapnik, "Statistical Learning Theory", Wiley, (1998).
  39. V. Vapnik, "The support vector method of function estimation", (1998).
  40. N. Chistianini and J. Shawe-Taylor, "An Introduction to Support Vector Machines, and other kernel-based learning methods", Cambridge University Press, (2000).
  41. N. Cristianini and J. Shawe-Taylor, "An Introduction to Support Vector Machines", Cambridge University Press, (2000).
  42. Anderson, J. A. , and Davis, J. , An introduction to neural networks. MIT, Cambride, 1995.
  43. Obenshain, M. K. , Application of data mining techniques to healthcare data. Infect. Control Hosp. Epidemiol. 25(8):690–695, 2004.
  44. Bellazzi, R. , and Zupan, B. , Predictive data mining in clinical medicine: current issues and guidelines. Int. J. Med. Inform. 77:81–97, 2008.
  45. Übeyli, E. D. , Comparison of different classification algorithms in clinical decision making. Expert syst 24(1):17–31, 2007.
  46. Kaur, H. , and Wasan, S. K. , Empirical study on applications of data mining techniques in healthcare. J. Comput. Sci. 2(2):194–200 2006.
  47. Romeo, M. , Burden, F. , Quinn, M. , Wood, B. , and McNaughton, D. , Infrared microspectroscopy and artificial neural networks in the diagnosis of cervical cancer. Cell. Mol. Biol. (Noisy-le-Grand, France) 44(1):179, 1998.
  48. Ball, G. , Mian, S. , Holding, F. , Allibone, R. , Lowe, J. , Ali, S. , et al. , An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers. Bioinformatics 18(3):395–404, 2002.
  49. Aleynikov, S. , and Micheli-Tzanakou, E. , Classification of retinal damage by a neural network based system. J. Med. Syst. 22(3):129–136, 1998.
  50. Potter, R. , Comparison of classification algorithms applied to breast cancer diagnosis and prognosis, advances in data mining,7th Industrial Conference, ICDM 2007, Leipzig, Germany, July 2007, pp. 40–49.
  51. Kononenko, I. , Bratko, I. , and Kukar, M. , Application of machine learning to medical diagnosis. Machine Learning and Data Mining: Methods and Applications 389:408, 1997.
  52. Sharma, A. , and Roy, R. J. , Design of a recognition system to predict movement during anesthesia. IEEE Trans. Biomed. Eng. 44(6):505–511, 1997.
  53. Einstein, A. J. , Wu, H. S. , Sanchez, M. , and Gil, J. , Fractal characterization of chromatin appearance for diagnosis in breast cytology. J. Pathol. 185(4):366–381, 1998.
  54. Brickley, M. , Shepherd, J. P. , and Armstrong, R. A. , Neural networks: a new technique for development of decision support systems in dentistry. J. Dent. 26(4):305–309, 1998.
  55. M. H. Dunham, "Data mining introductory and advanced topics", Upper Saddle River, NJ: Pearson Education, Inc. , (2003).
  56. Schwarzer, G. , Vach, W. , and Schumacher, M. , On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat. Med. 19:541–561, 2000.
  57. H. Hu, J. Li, A. Plank, H. Wang and G. Daggard, "A Comparative Study of Classification Methods For Microarray Data Analysis", Proc. Fifth Australasian Data Mining Conference (AusDM2006), Sydney, Australia. CRPIT, ACS, vol. 61, (2006), pp. 33-37.
  58. R. Potter, "Comparison of classification algorithms applied to breast cancer diagnosis and prognosis", advances in data mining, 7th Industrial Conference, ICDM 2007, Leipzig, Germany, (2007) July, pp. 40-49.
  59. L. Huang, H. C. Liao and M. C. Chen, "Prediction model building and feature selection with support vector machines in breast cancer diagnosis", Expert Systems with Applications, vol. 34, (2008), pp. 578-587.
  60. G. Beller, "The rising cost of health care in the United States: is it making the United States globally noncompetitive?", J. Nucl. Cardiol. , vol. 15, no. 4, (2008), pp. 481-482.
  61. M. U. Khan, J. P. Choi, H. Shin and M. Kim, "Predicting Breast Cancer Survivability Using Fuzzy Decision Trees for Personalized Healthcare", 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, (2008) August 20-24.
  62. L. Chang and C. H. Chen, "Applying decision tree and neural network to increase quality of dermatologic diagnosis", Expert Systems with Applications, Elsevier, vol. 36, (2009), pp. 4035-4041.
  63. R. Das, I. Turkoglub and A. Sengur, "Effective diagnosis of heart disease through neural networks ensembles", Expert Systems with Applications, vol. 36, (2009), pp. 7675-7680.
  64. I. Curiac, G. Vasile, O. Banias, C. Volosencu and A. Albu, "Bayesian Network Model for Diagnosis of Psychiatric Diseases", Proceedings of the ITI 2009 31st Int. Conf. on Information Technology Interfaces, Cavtat, Croatia, (2009) June 22-25.
  65. K. F. R. Liu and C. F. Lu, "BBN-Based Decision Support for Health Risk Analysis", Fifth International Joint Conference on INC, IMS and IDC, (2009).
  66. Avci, "A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier", Expert Systems with Applications, Elsevier, vol. 36, (2009), pp. 10618-10626.
  67. S. Gunasundari and S. Baskar, "Application of Artificial Neural Network in identification of Lung Diseases", Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on. IEEE, (2009).
  68. S. Soni and O. P. Vyas, "Using Associative Classifiers for Predictive Analysis in Health Care Data Mining", International Journal of Computer Applications (0975 – 8887), vol. 4, no. 5, (2010) July.
  69. S. W. Fei, "Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine", Expert Systems with Applications, Elsevier, vol. 37, (2010), pp. 6748-6752.
  70. O. Er, N. Yumusakc and F. Temurtas, "Chest diseases diagnosis using artificial neural networks", Expert Systems with Applications, vol. 37, (2010), pp. 7648-7655.
  71. Chuang, L. Y. , Wu, K. C. , Chang, H. W. and Yang, C. H. (2011) "Support Vector Machine-Based Prediction for Oral Cancer Using Four SNPs in DNA Repair Genes". Proceedings of the International Multi Conference of Engineers and Computer Scientists, Hong Kong,16-18 March 2011, 16-18.
  72. A. Bakar, Z. Kefli, S. Abdullah and M. Sahani, "Predictive Models for Dengue Outbreak Using Multiple Rulebase Classifiers", 2011 International Conference on Electrical Engineering and Informatics, Bandung, Indonesia, (2011) July 17-19.
  73. S. S. Moon, S. Y. Kang, W. Jitpitaklert and S. B. Kim, "Decision tree models for characterizing smoking patterns of older adults", Expert Systems with Applications, Elsevier, vol. 39, (2012), pp. 445-451.
  74. H. Jena, C. C. Wang, B. C. Jiangc, Y. H. Chub and M. S. Chen, "Application of classification techniques on development an early-warning systemfor chronic illnesses", Expert Systems with Applications, vol. 39, (2012), pp. 8852-8858.
  75. Chien and G. J. Pottie, "A Universal Hybrid Decision Tree Classifier Design for Human Activity Classification", 34th Annual International Conference of the IEEE EMBS San Diego, California USA, (2012) August 28-September 1.
  76. M. Shouman, T. Turner and R. Stocker, "Applying K-Nearest Neighbour in Diagnosing Heart Disease Patients", International Conference on Knowledge Discovery (ICKD-2012), (2012).
  77. Y. Liu, H. L. Chen, B. Yang, X. E. Lv, N. L. Li and J. Liu, "Design of an Enhanced Fuzzy k-nearest Neighbor Classifier Based Computer Aided Diagnostic System for Thyroid Disease", Journal of Medical System, Springer, (2012).
  78. Hattice and K. Metin, "A Diagnostic Software tool for Skin Diseases with Basic and Weighted K-NN", Innovations in Intelligent Systems and Applications (INISTA), (2012).
  79. M. J. Abdi and D. Giveki, "Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules", Engineering Applications of Artificial Intelligence, vol. 26, (2013), pp. 603-608.
  80. W. L. Zuoa, Z. Y. Wanga, T. Liua and H. L. Chenc, "Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach", Biomedical Signal Processing and Control, Elsevier, (2013), pp. 364-373.
  81. Rusdah and Edi Winarko, "Review on Data Mining Methods for Tuberculosis Diagnosis" Information Systems International Conference (ISICO), 2 – 4 December 2013.
  82. Marina Evrim Johnson and Nagen Nagarur, "Multi-stage methodology to detect health insurance claim fraud", Health Care Management Science, DOI 10. 1007/s10729-015-9317-3, Springer, 20 January 2015.
  83. Johannes Arthuur Govaert, Anne Charlotte Madeline van Bommel, Wouter Antonie van Dijk, Nicoline Johanneke van Leersum, Robertus Alexandre Eduard Mattheus Tollenaar and Michael Wilhemus Jacobus Maria Wouters, "Reducing Healthcare Costs Facilitated by Surgical Auditing: A Systematic Review", Worls J Surg, DOI 10. 1007/s00268-015-3005-9, Springer, 18 February 2015.
  84. Peng Z et al. , "An expression signature at diagnosis to estimate prostate cancer patients' overall survival", Prostate Cancer and Prostatic Disease (2014) 17, 81-90, doi 10. 1038/pcan. 2013. 57; January 2014, Nature.
  85. J. Fox, "Applied Regression Analysis, Linear Models, and Related Methods", (1997).
  86. Gennings, R. Ellis and J. K. Ritter, "Linking empirical estimates of body burden of environmental chemicals and wellness using NHANES data", http://dx. doi. org/10. 1016/j. envint. 2011. 09. 002,2011.
  87. P. A. Gutiérrez, C. Hervás-Martínez and F. J. Martínez-Estudillo, "Logistic Regression by Means of Evolutionary Radial Basis Function Neural Networks", IEEE Transactions on Neural Networks, vol. 22, no. 2, (2011), pp. 246-263.
  88. Divya and S. Agarwal, "Weighted Support Vector Regression approach for Remote Healthcare monitoring", IEEE-International Conference on Recent Trends in Information Technology, ICRTIT 2011, 978-1-4577-0590-8/11/$26. 00 ©2011 IEEE MIT, Anna University, Chennai, (2011) June 3-5.
  89. Gennings, R. Ellis and J. K. Ritter, "Linking empirical estimates of body burden of environmental chemicals and wellness using NHANES data", http://dx. doi. org/10. 1016/j. envint. 2011. 09. 002,2011.
  90. Xie et al. , "Predicting Days in Hospital Using Health Insurance Claims", IEEE Journal of Biomedical and Health Informatics, DOI 10. 1109/JBHI. 2015. 2402692,ISSN 2168-291, IEEE Transactions, 2015.
  91. J. Alapont, A. Bella-Sanjuán, C. Ferri, J. Hernández-Orallo, J. D. Llopis-Llopis and M. J. Ramírez-Quintana, "Specialised Tools for Automating Data Mining for Hospital Management", http://www. dsic. upv. es/~abella/papers/HIS_DM. pdf, (2005).
  92. K. Jain, M. N. Murty and P. J. Flynn, "Data Clustering: a review", ACM Compute, Surveys, vol. 31, (1996).
  93. Hamerly and C. Elkan, "Learning the K in K-means", Proceedings of the 17th Annual Conference on Neural Information Processing Systems, British Columbia, Canada, (2003).
  94. L. Lenert, A. Lin, R. Olshen and C. Sugar, "Clustering in the Service of the Public's Health", http://www-stat. stanford. edu/~olshen/manuscripts/helsinki. PDF.
  95. S. Belciug, F. Gorunescu, A. Salem and M. Gorunescu, "Clustering-based approach for detecting breast cancer recurrence", 10th International Conference on Intelligent Systems Design and Applications, (2010).
  96. J. Escudero, J. P. Zajicek and E. Ifeachor, "Early Detection and Characterization of Alzheimer's Disease in Clinical Scenarios Using Bioprofile Concepts and K-Means", 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, (2011) August 30-September 3.
  97. Yoo, I. , and Hu, X. , A comprehensive comparison study of document clustering for a biomedical digital library MDELINE. ACM/IEEE Joint Conference on Digital Libraries 11–15:220–229, 2006. Chapel Hill, NC, June 11–15, 2006.
  98. Yoo, I. , Hu, X. , and Song, I. -Y. , Biomedical ontology improves biomedical literature clustering performance: a comparison study. Int. J. Bioinform. Res. Appl. 3(3):414–428, 2007.
  99. M. E. Celebi, Y. A. Aslandogan and R. P. Bergstresser, "Mining Biomedical Images with Density-based Clustering", Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05), (2005).
  100. T. S. Chen, T. H. Tsai, Y. T. Chen, C. C. Lin, R. C. Chen, S. Y. Li and H. Y. Chen, "A Combined K-Means and Hierarchical Clustering Method for improving the Clustering Efficiency of Microarray", Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, (2005).
  101. Chipman and R. Tibshirani, "Hybrid hierarchical clustering with applications to microarray data", Biostatistics, vol. 7, no. 2, (2006), pp. 286-301.
  102. Bertsimas, M. V. Bjarnadóttir, M. A. Kane, J. C. Kryder, R. Pandey, S. Vempala and G. Wang, "Algorithmic prediction of health-care costs", Oper. Res. , vol. 56, no. 6, (2008), pp. 1382-1392.
  103. S. Belciug, "Patients length of stay grouping using the hierarchical clustering algorithm", Annals of University of Craiova, Math. Comp. Sci. Ser. , ISSN: 1223-6934, vol. 36, no. 2, (2009), pp. 79-84.
  104. J. J. Tapia, E. Morett and E. E. Vallejo, "A Clustering Genetic Algorithm for Genomic Data Mining", Foundations of Computational Intelligence, vol. 4 Studies in Computational Intelligence, vol. 204, (2009), pp. 249-275.
  105. T. H. A. Soliman, A. A. Sewissy and H. A. Latif, "A Gene Selection Approach for Classifying Diseases Based on Microarray Datasets", 2nd International Conference on Computer Technology and Development (lCCTD 2010), (2010).
  106. Schulam et al. , "Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery", Associations for the Advancements of Artificial Intelligence, 2015.
  107. Fletcher Lu and J. Efrim Boritz, "Detection Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions", Machine Learning: EMCL 2005 16th European Conference on Machine Learning, Porto, Portugal,October 3-7, 2005 Volume 3720, pages 633-640, Springer.
  108. Peng Y, Kou G, Sabatka A, Chen Z, Khazanchil D and Shi Y," Application Of clustering methods to health insurance fraud detection", Int Conf Serv Syst Serv Manag 1:116-120, 2006.
  109. Piatetsky-Shapiro, G. , Discovery, analysis, and presentation of strong rules. In: Piatetsky-Shapiro, G. , (Ed. ), Knowledge Discovery in Databases. AAAI/MIT Press, 1991, pp. 229–248.
  110. Agrawal, R. , Imielinski, T. , and Swami, A. , Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD International Conference on the Management of Data. ACM, Washington DC, pp. 207–216, 1993.
  111. Agrawal, R. , and Srikant, R. , Fast algorithms for mining association rules, Proceedings of the 20th International Conference on Very Large Data Bases (VLDB'94). Morgan Kaufmann, Santiago, pp. 487–499, 1994.
  112. J. Yanqing, H. Ying, J. Tran, P. Dews, A. Mansour and R. Michael Massanari, "Mining Infrequent Causal Associations in Electronic Health Databases", 11th IEEE International Conference on Data Mining Workshops, (2011).
  113. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules", VLDB, Chile, ISBN 1-55860-153-8, (1994) September 12-15, pp. 487-99.
  114. U. Abdullah, J. Ahmad and A. Ahmed, "Analysis of Effectiveness of Apriori Algorithm in Medical Billing Data Mining", 2008 International Conference on Emerging Technologies, IEEE-ICET 2008, Rawalpindi, Pakistan, (2008) October 18-19.
  115. M. Patil, R. C. Joshi and D. Toshniwal, "Association rule for classification of type -2 diabetic patients", Second International Conference on Machine Learning and Computing, (2010).
  116. J. Yanqing, H. Ying, J. Tran, P. Dews, A. Mansour and R. Michael Massanari, "Mining Infrequent Causal Associations in Electronic Health Databases", 11th IEEE International Conference on Data Mining Workshops, (2011).
  117. M. Ilayaraja and T. Meyyappan, "Mining Medical Data to Identify Frequent Diseases using Apriori Algorithm", Proceedings of the 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, (2013) February 21-22.
  118. J. Nahar, T. Imam, K. S. Tickle and Y. P. Chen, "Association rule mining to detect factors which contribute to heart disease in males and females", Expert Systems with Applications, vol. 40, pp. 1086-1093, (2013).
  119. Eiko Kai et al. , "Enpowering the healthcare worker using the Portable Health Clinic", IEEE Transactions, DOI 10. 1109/AINA. 2014. 108, 2014.
  120. Divya et al. ," A Survey on Data Mining Approaches for Healthcare", International Journal Of Bio-Science and Bio-Technology Vol. 5, No. 5 (2013),pp. 241-266 http://dx. doi. org/10. 14257/ijbsbt. 2013. 5. 5. 25.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Classification Clustering Association Healthcare

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