Support vector machine the most fruitful algorithm for prognosticating heart disorder

  • Authors

    • M Murugesan
    • R Elankeerthana
    2018-05-07
    https://doi.org/10.14419/ijet.v7i2.26.12533
  • Heart Disease, Data Mining, Decision Tree, J48, Naive Bayes, K-NN, Lazy IBK, Support Vector Machines.

  • Abstract

    One of the wealthiest areas of research is Data mining that is more popular in healthcare organizations. Heart disease is the main outcome of death in the human society over the recent years. Heart disease is serious life threatening diseases that result to death. In order to save a pan-tient’s life, the doctors and medical examiners are being taking many efforts. The consultant of doctor’s determination can make without the advice of specialists because of the software develop by the advancement in computer technology. In most of the papers, Data mining tech-niques used in the existing method in the research are Naive Bayes, Decision tree, J48, K-Nearest Neighbor (K-NN) (or) Lazy IBK algo-rithms to predict heart diseases. In this paper, support vector machines (SVM) technique will produce the most accuracy prediction rate for heart diseases while comparing to all the other techniques used in data mining.

     

  • References

    1. [1] Ajad Patel, Sonali Gandhi, Swetha Shetty,Prof. Bhanu Tekwani.(2017). Heart Disease Prediction Using Data Mining, 4(1):1705-1707.

      [2] Akshaya, Nivetha Priya.M., Ms. Kartika’s. (2015). Data Mining Techniques to Diagnose Heart Diseases, 2(4):132-134.

      [3] Dr. P. C. Agrawal and Aparna Chaturvedi.(2015).An Analytical Study of Data Mining with the Help of UML Model and its Application,4(3):87-90.

      [4] Era Singh Kajal and Ms. Nishika.(2016).Prediction of Heart Disease using Data Mining Techniques,2(3):1-7.

      [5] Fabio Mendoza Palechor*,Alexis De la Hoz Manotas,Paola Ariza Colpas, Jorge Sepulveda Ojeda, Roberto Morales Ortega and Marlon Piñeres Melo.(2016) Cardiovascular Disease Analysis Using Supervised and Unsupervised Data Mining Techniques,12(2):81-90 .

      [6] H K Shifali, Dr. B. Srinivasu, Rajashekar Shastry and B N Ranga Swamy. (2017). Mining of Medical Data to Identify Risk Factors of Heart Disease Using Frequent Itemset, 4(1):1613-1619.

      [7] K.Manimekalai.(2016). A Proficient Heart Disease Prediction Method Using Different Data Mining Tools, 6(3):2676-2680.

      [8] P.Santhi, V.Murali Bhaskaran “Performance of Clustering Algorithms in Healthcare Databaseâ€, International Journal for Advances in Computer Science, Volume 2, Issue 1, March 2010

      [9] Megha Shahi and Er. Rupinder Kaur Gurm. (2017). Heart Disease Prediction System Using Data Mining Techniques - A Review, 3(4):73-77.

      [10] Prof. Mamta Sharma, Farheen Khan, Vishnupriya Ravichandran. (2017). Comparing Data Mining Techniques Used For Heart Disease Prediction, 4(6):1161-1167.

      [11] S.Keerthi, S.Dhivya, Comparison of RVM and SVMClassifier Performance in Analysing the Tuberclosis in Chest X Ray. Vol.10, No.36 Sep (2017). 0974-5572.

      [12] Rani Geetika. (2016).Hierarchical Clustering Techniques In Data Mining: A Comparative Study, 2(1):31-36.

      [13] R. Thanigaivel and K. Ramesh Kumar. (2016).Boosted Apriori: an Effective Data Mining Association Rules for Heart Disease Prediction System, 24(1):192-200.

      [14] Sharan Monica L and Sathees Kumar B. (2016).Analysis of Cardiovascular Heart Disease Prediction Using Data Mining Techniques, 4(1):55-58.

      [15] Shashikant Ghumbre, Chetan Patil, and Ashok Ghatol. (2011).Heart Disease Diagnosis using Support Vector Machine, 84-88.

      [16] Sonam Nikhar and A. M. Karandikar. (2016). Prediction of Heart Disease Using Data Mining Techniques - A Review, 3(2):1291-1293.

      [17] Ms S. Vijayarani and Ms M. Muthulakshmi (2013).Comparative Analysis of Bayes and Lazy Classification Algorithms, 2(8):3118-3124.

      [18] Xindong Wu · Vipin Kumar · J. Ross Quinlan · Joydeep Ghosh ·Qiang Yang ·Hiroshi Motoda offrey J. McLachlan · Angus Ng · Bing Liu ·Philip S. Yu ·Zhi-Hua Zhou · Michael Steinbach · David J. Hand and Dan Steinberg. (2007). Top 10 algorithms in data mining, 14:1-37.

      [19] Dr.P.Santhi, K.Deepa, Classification System for Identifying the chemical Structure Using the Support Vector Machine. Vol.03, No.1, June (2017). 2349-7866.

      [20] S.Thilagamani, N. Shanthi,†Object Recognition Based on Image Segmentation and Clusteringâ€, Journal of Computer Science,Vol. 7,No.11,pp. 1741-1748, 2011.

      [21] S.Thilagamani , N. Shanthi,†Gaussian and gabor filter approach for object segmentationâ€, Journal of Computing and Information Science in Engineering,Vol.14,Issue 2, pp. 021006,2014.

      [22] Dr.P.Santhi, S.Kiruthika,†Lung Based Disease prediction Using Lobe Segmentation Based on Neural Networksâ€, International Journal of Pure and Applied Mathematicsâ€, Vol.118, No.8, PP. 499-504, 2018.

      [23] P.Santhi, R.Vikram,†Implementation Of Classification System Using Density Clustering Based Gray Level Co Occurrence Matrix (DGLCM) For Green Bio Technologyâ€, International Journal of Pure and Applied Mathematicsâ€,Vol.118, No.8,PP. 191-195,2018.

      [24] S.Thilagamani, V.Manochitra,â€An Intelligent Region-Based Method for Detecting Objects from Natural Imagesâ€, International Journal of Pure and Applied Mathematicsâ€, Vol.118, No.8, PP.473-478, 2018.

      [25] S. Thilagamani, N.Shanthi, “A Novel Recursive Clustering Algorithm for Image Oversegmentationâ€, European Journal of Scientific Research, Vol.52, No.3, pp.430-436, 2011.

      [26] S.Thilagamani and S. Uma Mageshwari,†Risk appraisal for cardiovascular disease among selected young adult women in Coimbatore, Indiaâ€, Indian Journal of Science and Technology, Vol. 3, No. 6 ,PP.672-675,2010

      [27] T. Mekala, P. Nandhini,†Modified Agglomerative Clustering for Web Users Navigation Behaviorâ€, International Journal of Advanced Networking and Applications, Vol. 05, Issue: 01, PP.1842-1846, 2013.

      [28] S.Thilagamani,N. Shanthi, “Literature survey on enhancing cluster qualityâ€, International Journal on Computer Science and Engineering Vol. 02, No. 06, pp1999-2002, 2010.

      [29] K.Deepa, Y.Naveen Raj, “Enhancing the Performance in WSN using Distributed Tracking Algorithmâ€, International Journal of Pure and Applied Mathematics Vol. 118, No. 9 2018, 717-722.

      [30] G.Ranjith , M.Vinoth, “An Unified Approach for Effective Use of Cloud Metering Serviceâ€, International Journal of Pure and Applied Mathematics Vol. 118, No. 9 2018, 801-806.

      [31] E.T.Venkatesh,P.Tangaraj, S. Chitra, “Classification of cancer gene expressions from micro-array analysisâ€, International Conference Innovative Computing Technologies (ICICT), 2010.

      [32] V.Baby Deepa, P.Thangaraj, S.Chitra,†Investigating principal component analysis for classification of EEG dataâ€, International Conference on Networking and Information Technology (ICNIT), PP.461-464, 2010.

      [33] Hema.C.R, Paulraj.M.P & Ramkumar.S, “Classification of Eye Movements Using Electrooculography and Neural Networksâ€, International Journal of Human Computer Interaction, Vol.5 (4), pp.51-63, 2014.

      [34] Hema, C. R., Ramkumar, S., & Paulraj, M. P. , “Idendifying Eye Movements using Neural Networks for Human Computer Interactionâ€, International Journal of Computer Applications, 105(8), pp 18-26, 2014.

      [35] S.Ramkumar, K.SatheshKumar, G.Emayavaramban, â€EOG Signal Classification Using Neural Network for Human Computer Interactionâ€, International Journal of Computer Theory and Applications, Vol.9(24) , pp.223-231, 2016

      [36] Ramkumar, Dr.K.Satheshkumar and G.Emayavaramban†Nine States HCI using Electrooculogram and Neural Networksâ€, IJET, Vol. 8(6), pp. 3056-3064, Jan 2017.

      [37] S.Ramkumar, K.Sathesh Kumar G.Emayavaramban,†A Feasibility Study on Eye Movements Using Electrooculogram Based HCI†IEEE- International Conference on Intelligent Sustainable Systems, pp.384-388, Dec-2017.

      [38] G.Emayavaramban, S.Ramkumar, A.Amudha and K.Sathesh Kumar “Classification Of Hand Gestures Using FFNN And TDNN Networksâ€, International Journal of Pure And Applied Mathematics, Vol.118 (8) Pp. 27-32, Jan 2018.

      [39] S.Ramkumar, K.Sathesh Kumar, T.Dhiliphan Rajkumar, M.Ilayaraja, K.Shankar, “A review-classification of electrooculogram based human computer interfacesâ€, Biomedical Research, 29(6), Pp. 1078-1084, April 2018.

  • Downloads

  • How to Cite

    Murugesan, M., & Elankeerthana, R. (2018). Support vector machine the most fruitful algorithm for prognosticating heart disorder. International Journal of Engineering & Technology, 7(2.26), 48-52. https://doi.org/10.14419/ijet.v7i2.26.12533

    Received date: 2018-05-06

    Accepted date: 2018-05-06

    Published date: 2018-05-07