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A Study of Data Mining Techniques Accuracy for Healthcare

by Hilal Almarabeh, Ehab F. Amer
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 3
Year of Publication: 2017
Authors: Hilal Almarabeh, Ehab F. Amer
10.5120/ijca2017914338

Hilal Almarabeh, Ehab F. Amer . A Study of Data Mining Techniques Accuracy for Healthcare. International Journal of Computer Applications. 168, 3 ( Jun 2017), 12-17. DOI=10.5120/ijca2017914338

@article{ 10.5120/ijca2017914338,
author = { Hilal Almarabeh, Ehab F. Amer },
title = { A Study of Data Mining Techniques Accuracy for Healthcare },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 168 },
number = { 3 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 12-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume168/number3/27854-2017914338/ },
doi = { 10.5120/ijca2017914338 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:15:08.298150+05:30
%A Hilal Almarabeh
%A Ehab F. Amer
%T A Study of Data Mining Techniques Accuracy for Healthcare
%J International Journal of Computer Applications
%@ 0975-8887
%V 168
%N 3
%P 12-17
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is the analysis of large datasets to discover patterns and use those patterns to predict the likelihood of the future events. Data mining is becoming a very important field in healthcare sectors and it holds great potential for the healthcare industry. This paper presents an overview of current research being carried out using data mining techniques in different medical areas such as heart disease, diabetes, breast and lung cancer and skin disease by using different data mining techniques to find the best method of prediction and accuracy.

References
  1. Thenmozhi, K. and P. Deepika, Heart disease prediction using classification with different decision tree techniques. Int. J. Eng. Res. Gen. Sci, 2014. 2(6).
  2. Han, J., J. Pei, and M. Kamber, Data mining: concepts and techniques. 2011: Elsevier.
  3. Gupta, S., D. Kumar, and A. Sharma, Data mining classification techniques applied for breast cancer diagnosis and prognosis. Indian Journal of Computer Science and Engineering (IJCSE), 2011. 2(2): p. 188-195.
  4. Kumari, M. and S. Godara, Comparative study of data mining classification methods in cardiovascular disease prediction 1. 2011.
  5. Ha, S.H. and S.H. Joo, A hybrid data mining method for the medical classification of chest pain. International Journal of Computer and Information Engineering, 2010. 4(1): p. 33-38.
  6. Kavitha, K., K. Ramakrishnan, and M.K. Singh, Modeling and design of evolutionary neural network for heart disease detection. International Journal of Computer Science Issues, 2010. 7(5): p. 272-283.
  7. Parvathi, R. and S. Palaniammalì, An improved medical diagnosing technique using spatial association rules. European Journal of Scientific Research ISSN, 2011: p. 49-59.
  8. Chao, S. and F. Wong. An incremental decision tree learning methodology regarding attributes in medical data mining. in Machine Learning and Cybernetics, 2009 International Conference on. 2009. IEEE.
  9. Habrard, A., M. Bernard, and F. Jacquenet. Multi-relational Data Mining in medical databases. in Conference on Artificial Intelligence in Medicine in Europe. 2003. Springer.
  10. Duan, L., W.N. Street, and E. Xu, Healthcare information systems: data mining methods in the creation of a clinical recommender system. Enterprise Information Systems, 2011. 5(2): p. 169-181.
  11. Kumar, D.S., G. Sathyadevi, and S. Sivanesh, Decision support system for medical diagnosis using data mining. International Journal of Computer Science Issues, 2011. 8(3): p. 147-153.
  12. Werts, N. and M. Adya, Data Mining in Healthcare: Issues and a Research Agenda. AMCIS 2000 Proceedings, 2000: p. 98.
  13. Purusothaman,G. and P. Krishnakumari, A survey of data mining techniques on risk prediction: Heart disease. Indian Journal of Science and Technology, 2015. 8(12).
  14. Y. W. Xing, J. Wang, Z. H. Zhao, and Y. H. Gao., "Combination data mining methods with new medical data to predicting outcome of coronary heart disease," presented at the International Conference on Convergence Information Technology, 2007.
  15. M. Anbarsi, E. Anupriya, and N. Iyengar, "Enhanced prediction of heart disease with feature subset selection using genetic algorithm," International Journal of Engineering Science and Technology, vol. 2, no.10, pp. 5370-5376, 2010.
  16. R. Alizadehsani, J. Habibi, B. Bahadorian, H. Mashayekhi, A. Ghandeharioun, and R. Boghrati, et al., "Diagnosis of coronary arteries stenosis using data mining," J Med Signals Sens, vol. 2, pp. 153-9, Jul 2012.
  17. K. Srinivas, G. R. Rao, and A. Govardhan, "Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques," presented at the 5th International Conference on Computer Science and Education, 2010.
  18. 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.
  19. Y. Kangwanariyakul, C. Nantasenamat, T. Tantimongcolwat and T. Naenna, “Data Mining of Magneto cardiograms For Prediction of Ischemic Heart Disease”, EXCLI Journal, (2010).
  20. Jaekwon Kim, MS, Jongsik Lee, PhD, and Youngho Lee, PhD, , Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree, Health inform Res, Jul(2015), Vol.21, no.3.
  21. Satyam Shukla, Dharmendra Lal Gupta, Bakshi Rohit Prasad, Comparative Study of Recent Trends on Cancer Disease Prediction Usind Data Mining Techniques, Internationl Journal of Database Theory and Application, Vol.9, no.6, (2016), pp.107-118.
  22. V.Krishnaiah , Dr.G.Narsimha, Dr.N.Subhash Chandra, Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques, International Journal of Computer Science and Information Technologies, Vol. 4 , no.1. , (2013), pp. 39 - 45.
  23. Soumadip Ghosh, Sujoy Mondal, Bhaskar Ghosh, A comparative study of breast cancer detection based on SVM and MLP BPN classifier,  First International Conference on Automation, Control, Energy and Systems (ACES), IEEE, (2014 ).
  24. Gouda I. Salama , M.B.Abdelhalim , and Magdy Abd-elghany Zeid, Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers, International Journal of Computer and Information Technology Vol.1, no.1, September (2012), pp. 2277 – 0764.
  25. Zahra Nematzadeh,  Roliana Ibrahim,  Ali Selamat, Comparative studies on breast cancer classifications with k-fold cross validations using machine learning techniques,  Control Conference (ASCC), 2015 10th Asian, IEEE, June (2015).
  26. Hiba Asria ,Hajar Mousannif, Hassan Al Moatassime, Thomas Noel, Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis, The 6th International Symposium on Frontiers in Ambient and Mobile Systems (FAMS 2016), pp. 1064 – 1069.
  27. Mohammed Abdullah Hassan Al-Hagery, Classifiers’ Accuracy Based on Breast Cancer Medical Data And Data Mining Techniques, International Journal of Advanced Biotechnology and Research (IJBR), Vol.7, Ino.2, (2016), pp760-772.
  28. G. Ravi Kumar, Dr. G. A. Ramachandra, K.Nagamani, An Efficient Prediction of Breast Cancer Data using Data Mining Techniques, International Journal of Innovations in Engineering and Technology (IJIET), Vol. 2 , no. 4, August (2013).
  29. Subrata Kumar Mandal, Performance Analysis Of Data Mining Algorithms For Breast Cancer Cell Detection Using Naïve Bayes, Logistic Regression and Decision Tree, International Journal Of Engineering And Computer Science , Vol.6, no.2 , Feb. (2017), pp. 20388-20391.
  30. A.Priyanga, S.Prakasam, Ph.D, Effectiveness of Data Mining - based Cancer Prediction System (DMBCPS), International Journal of Computer Applications , Vol. 83 – No 10, December (2013), pp. 0975 – 8887.
  31. Thangaraju , Barkavi , Karthikeyan, Mining Lung Cancer Data for Smokers and Non-Smokers by Using Data Mining Techniques, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3,no. 7, July (2014).
  32. T. Christopher, "A Study on Mining Lung Cancer Data for Increasing or Decreasing Disease Prediction Value by Using Ant Colony Optimization Techniques", Proceedings of the UGC Sponsored National Conference on Advanced Networking and Applications, (2015).
  33. Y.-C. Chen, W.-C. Ke, H.-W. Chiu Risk classification of cancer survival using ANN with gene expression data from multiple laboratories Compute Biol Med, Vol. 48, (2014), pp. 1–7.
  34. Yuvarani Mrs. P., Analysis of Lung Cancer Detection Algorithms - A Survey, Discovery the International journal, (2016), ISSN 2278 – 5469, EISSN 2278-5450.
  35. P. Thamilselvan, Dr. J. G. R. Sathiaseelan, An enhanced k nearest neighbor method to detecting and classifying MRI lung cancer images for large amount data, International Journal of Applied Engineering Research ISSN 0973-4562 Vol.11, No.6 (2016), pp 4223-4229
  36. K. Jayasurya, G. Fung, S. Yu, C. Dehing-Oberije, D. De Ruysscher, A. Hope, W. De Neve, Y. Lievens, P. Lambin, A. L. A. J. Dekker, Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy, T he International Journal of Medical Physics and Research, Vol. 37, no, 4, (2010).
  37. Ankit Agrawal, Sanchit Misra, Ramanathan Narayanan, Lalith Polepeddi, Alok Choudhary, A Lung Cancer Outcome Calculator Using Ensemble Data Mining on SEER Data, ACM, (2011).
  38. Jinsa Kuruvilla, K. Gunavathi , Lung cancer classification using neural networks for CT images, ELSEVIER, (3013).
  39. Miroslav Marinov, Abu Saleh Mohammad Mosa, M.S, Illhoi Yoo, Ph.D, and Suzanne Austin Boren, Data-Mining Technologies for Diabetes: A Systematic Review, Journal of Diabetes Science and Technology, Vol.5, no.6, (2011).
  40. Rakesh Motka, Viral Parmar, Balbindra Kumar, A. R. Verma, “ Diabetes Mellitus Forecast Using Different Data Mining Techniques”, International conference on computer and Communication Technology, IEEE (2013).
  41. Ravi sankal, T.Jayakumari, ―prognosis of diabetes using data mining approach fuzzy c mean clustering and support vector machine‖-International journal of computer Trends and Technology (IJCIT) vol 11 number 2 may 2014.
  42. P.Radha, Dr.B.Srinivasan ―International Journal of Innovative science science Engineering & Technology Vol. 1 issue 6 august 2014.
  43. Sadri sa’di, Amanj maleki, Rami hashemi, Zahra panbechi, kamal chalabi, ―Comparison of Data Mining algorithms in the diagnosis of type- II diabetes‖ – International journal on computational science & Application(IJCSA) vol 5 no 5 october 2015.
  44. Durairaj M., Kalaiselvi G.,―Prediction of Diabetes Using Soft Computing Techniques- a Survey‖ International journal Of scientific & technology research, volume 4, ISSUE 03, PP.190-192, 2015.
  45. Dr.M.Renuka Devi, J.Maria Shyla, ―Analysis of various data mining Techniques to predict diabetes Mellitus‖,- International journal of Applied engineering Research‖, ISSn 0973-4562 vol 11 number 1 2016.
  46. Sudesh Rao, V. Arun Kumar, “Applying Data mining Technique to predict the diabetes of our future generations”, ISRASE eXplore digital library, 2014.
  47. Mohtaram Mohammadi, Mitra Hosseini, Hamid Tabatabaee, “Using Bayesian Network for the prediction and Diagnosis of Diabetes”, MAGNT Research Report, vol.2 (5), pp.892-902.
  48. P. Radha, Dr. B. Srinivasan, “ Predicting Diabetes by consequence the various Data mining Classification Techniques”, International Journal of Innovative Science, Engineering & Technology, vol. 1 Issue 6, August 2014, pp. 334-339.
  49. Madeeh Nayer Algedawy, Detecting Diabetes Mellitus using Machine Learning Ensemble, International Journal of Computer Systems (ISSN: 2394-1065), Volume 03– Issue 12, December, 2016.
  50. Nisha Yadav, Nisha Yadav, Virender Kumar Narang, Skin Diseases Detection Models using Image Processing: A Survey, International Journal of Computer Applications (0975 – 8887) Vol.137 No.12, March 2016.
  51. A.A.L.C. Amarathunga, E.P.W.C. Ellawala, G.N. Abeysekara, C. R. J. Amalraj, Expert System For Diagnosis Of Skin Diseases, INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 4, ISSUE 01, JANUARY 2015, ISSN 2277-8616 .
  52. Krupal S. Parikh, Trupti P. Shah , RahulKrishna Kota and Rita Vora, Diagnosing Common Skin Diseases using Soft Computing Techniques, International Journal of Bio-Science and Bio-Technology Vol.7, No.6 (2015), pp.275-286.
  53. 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.
  54. Hatice cataloluk, Metin kesler,” A Diagnostic Software Tool for Skin Diseases with Basic and Weighted K-NN” 978-1-4673-1448-0/12/$31.00 © IEEE 2012.
  55. L.Chang & C.H.Chen, “APPLYING DECISION TREE AND NEURAL NETWORK TO INCREASE QUALITY OF DERMATOLOGIC DIAGNOSIS”, Expert Systems with Applications- Elsevier, Volume: 36, pp. 4035-4041, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Healthcare Disease diagnosis Data mining techniques Accuracy.

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