A Novel Approach to Predict Disease and Avoid Congestion in Data Mining Using Genetic Algorithm

  • Authors

    • S. Ramasamy
    • Dr. K. Nirmala
    https://doi.org/10.14419/ijet.v7i3.20.26740
  • Data Mining, Association Rule, Keyword Based Clustering, Genetic algorithm, Classification.

  • Abstract

    Presently days the health segment contains concealed information that can be critical in deciding. It is troublesome for medical professionals to anticipate the disease as it is really an intricate errand that requires experience and information. The objective of the examination is to anticipate conceivable disease from the patient dataset utilizing data mining systems and to organize the patients based on their genuine conditions to lessen the blockage in the system. In this paper we propose proficient acquainted order algorithm utilizing genetic methodology for disease expectation. The principle inspiration for utilizing genetic algorithm as a part of the disclosure of abnormal state forecast rules is that the found rules are exceptionally conceivable, having high prescient precision and of high interestingness values.

     

     

  • References

    1. [1] S. Vijayarani* and S. Sudha “An Efficient Clustering Algorithm for Predicting Diseases from Hemogram Blood Test Samples†Indian Journal of Science and Technology, Vol 8(17), August 2015.

      [2] Shakeel PM, Baskar S, Dhulipala VS, Mishra S, Jaber MM., “Maintaining security and privacy in health care system using learning based Deep-Q-Networksâ€, Journal of medical systems, 2018 Oct 1;42(10):186.https://doi.org/10.1007/s10916-018-1045-z

      [3] G. Purusothaman* and P. Krishnakumari “A Survey of Data Mining Techniques on Risk Prediction: Heart Disease†Indian Journal of Science and Technology, Vol 8(12), DOI: 10.17485/ijst/2015/v8i12/58385, June 2015.

      [4] Jyoti Soni Ujma Ansari Dipesh Sharma “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction†international Journal of Computer Applications, Volume 17– No.8, March 2011

      [5] Shakeel PM, Baskar S, Dhulipala VS, Jaber MM., “Cloud based framework for diagnosis of diabetes mellitus using K-means clusteringâ€, Health information science and systems, 2018 Dec 1;6(1):16.https://doi.org/10.1007/s13755-018-0054-0

      [6] Himigiri. Danapana, M. Sumender Roy,‖ Effective Data Mining Association Rules for Heart Disease Prediction System‖ IJCST Vol. 2,Issue 4, Oct . - Dec. 2011.

      [7] Fariba Shadabi and Dharmendra Sharma,‖ Artificial Intelligence and Data Mining Techniques in Medicine – Success Stories‖ International Conference on BioMedical Engineering and Informatics- 2008.

      [8] Shakeel, P.M., Tolba, A., Al-Makhadmeh, Zafer Al-Makhadmeh, Mustafa Musa Jaber, “Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networksâ€, Neural Computing and Applications,2019,pp1-14.https://doi.org/10.1007/s00521-018-03972-2

      [9] J. Liu, Y.-T. HSU, and C.-L. Hung, “Development of Evolutionary Data Mining Algorithms and their Applications to Cardiac Disease Diagnosis,†in WCCI 2012 IEEE World Congress on Computational Intelligence, 2012, pp. 10–15.

      [10] P. Chandra, M. . Jabbar, and B. . Deekshatulu, “Prediction of Risk Score for Heart Disease using Associative Classification and Hybrid Feature Subset Selection,†in 12th International Conference on Intelligent Systems Design and Applications (ISDA), 2012, pp. 628– 634.

      [11] P. Mohamed Shakeel; Tarek E. El. Tobely; Haytham Al-Feel; Gunasekaran Manogaran; S. Baskar., “Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensorâ€, IEEE Access, 2019, Page(s): 1

      [12] S. U. Amin, K. Agarwal, and R. Beg, “Genetic Neural Network Based Data Mining in Prediction of Heart Disease Using Risk Factors,†in Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013), 2013, no. Ict, pp. 1227– 1231.

      [13] Preeth, S.K.S.L., Dhanalakshmi, R., Kumar, R.,Shakeel PM.An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system.Journal of Ambient Intelligence and Humanized Computing.2018:1–13. https://doi.org/10.1007/s12652-018-1154-z

      [14] Zhao, Q., Rezaei, M., Chen, H., Franti, and P.: Keyword clustering for automatic categorization. Pattern Recognition (ICPR), 2012 21st International Conference on. IEEE, (2012).

      [15] Michael Pucher, F. T. W.: Performance Evaluation of WordNet-based Semantic Relatedness Measures for Word Prediction in Conversational Speech. (2004).

      [16] K. Sudhakar, “Study of Heart Disease Prediction using Data Mining,†vol. 4, no. 1, pp. 1157–1160, 2014.

      [17] R. Chitra and V. Seenivasagam, “REVIEW OF HEART DISEASE PREDICTION SYSTEM USING DATA MINING AND HYBRID INTELLIGENT TECHNIQUES,†Journal on Soft Computing (ICTACT), vol. 3, no. 4, pp. 605–609, 2013.

      [18] Shanta kumar, B.Patil,Y.S.Kumaraswamy, “Predictive data mining for medical diagnosis of heart disease prediction†IJCSE Vol .17, 2011

      [19] M. Anbarasi et. al. “Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithmâ€, International Journal of Engineering Science and Technology Vol. 2(10), 5370-5376 ,2010.

      [20] Hnin Wint Khaing, “Data Mining based Fragmentation and Prediction of Medical Dataâ€, IEEE, 2011.

      [21] MA.Jabbar, Priti Chandra, B.L.Deekshatulu..:Cluster based association rule mining for heart attack prediction,JATIT,vol 32,no2,(Oct 2011)

      [22] Ping Ning tan, Steinbach, vipin Kumar. : Introduction to Data Mining, Pearson Education, (2006).

      [23] Picek, S., Golub, M.: On the Efficiency of Crossover Operators in Genetic Algorithms with Binary Representation. In: Proceedings of the 11th WSEAS International Conference on Neural Networks (2010)

      [24] Aswathy Wilson, Gloria Wilson, Likhiya Joy K “ Heart disease prediction using data mining techniquesâ€

      [25] Hnin Wint Khaing, “Data Mining based Fragmentation and Prediction of Medical Dataâ€, International Conference on Computer Research and Development, ISBN: 978-1-61284-840-2,2011

  • Downloads

  • How to Cite

    Ramasamy, S., & K. Nirmala, D. (2018). A Novel Approach to Predict Disease and Avoid Congestion in Data Mining Using Genetic Algorithm. International Journal of Engineering & Technology, 7(3.20), 738-742. https://doi.org/10.14419/ijet.v7i3.20.26740

    Received date: 2019-01-29

    Accepted date: 2019-01-29