A Novel Approach for Prediction of Heart Disease: Machine Learning Techniques

  • Authors

    • V Srinivas
    • K Aditya
    • G Prasanth
    • R G.Babukarthik
    • S Satheeshkumar
    • G Sambasivam
    2018-05-31
    https://doi.org/10.14419/ijet.v7i2.32.15380
  • Machine learning, Cloud computing, Cardiac, Backpropagation, Optimization
  • Heart disease and machine learning are the two different words where one is related to medical field and another one to artificial intelligence. In medical filed most of them are facing the problems with the heart disease and machine learning is developing area in computer science. Heart disease is general called cardiac disease where it gives the more data or information, it is to be collected to give the reports for the patients and the machine learning also requires the data for predicting and to solve the problems. Machine learning techniques are used in prediction of heart diseases where it gives the faster prediction with less computation time and better accuracy to progress their health. Heart disease prediction requires lot of data for predicting and in cloud computing also we have more data and the data available in cloud it is difficult to analyze. So we use machine learning algorithms or techniques to predict the heart disease and the in the similar way we can apply these algorithms or techniques to predict or analyze the data that is available in cloud. In this paper we are going to use machine learning algorithms called Backpropagation Algorithm and later we use optimization algorithm later. Backpropagation algorithm deals with the artificial neural networks. Backpropagation is a method used to calculate the error contribution of each neuron after a batch of data (in image recognition, multiple images) is processed. This is used by an enveloping optimization algorithm to adjust the weight of each neuron, completing the learning process for that case. Machine learning algorithms and techniques are used for recognize the intensity of risk issues in humans and it helps the patients to take safety measures in well advances to save the patient’s life.

     

  • References

    1. [1] Stephanie MW, John LJ. New genetic insights into congenital heart disease. J Clin Exp Cardiolog 2012; 15(S8):003.

      [2] Pierpont ME, Basson CT, Benson Jr DW, Gelb BD, Giglia TM, Goldmuntz E, et al. Genetic basis for congenital heart defects: current knowledge: a scientific statement from the American Heart Association Congenital Cardiac Defects Committee, Council on Cardiovascular Disease in the Young. Circulation 2007;115:3015–38 .

      [3] Jenkins KJ, Correa A, Feinstein JA, Botto L, Britt AE, Daniels SR, et al. Noninherited risk factors and congenital cardiovascular defects. Circulation 2007;115:2995–3014.

      [4] Botto LD, Mulinare J, Erickson JD. Do multivitamin or folic acid supplements reduce the risk for congenital heart defects? Evidence and gaps. Am J Med Genet 2003;121A:95–101 .

      [5] Bailey LB, Berry RJ. Folic acid supplementation and the occurrence of congenital heart defects, orofacial clefts, multiple births, and miscarriage. Am J Clin Nutr 2005;81:1213S–7S .

      [6] Shaw GM, O’Malley CD, Wasserman CR, Tolarova MM, Lammer EJ. Maternal periconceptional use of multivitamins and reduce risk for conotruncal heart defects and limb deficiencies among offspring. Am J Med Genet 1995;59:536–45 .

      [7] Hernandez-Diaz S, Werler MM, Walker AM, Mitchell AA. Folic acid antagonists during pregnancy and the risk of birth defects. N Engl J Med 2000;343:1608–14 .

      [8] Hobbs CA, Cleves MA, Melnyk S, Zhao W, James SJ. Congenital heart defects and abnormal maternal biomarkers methionine and homocysteine metabolism. Am J Clin Nutr 2005;81:147–53.

      [9] Li Y, Wu X, Xu J, Qian Y, Zhou C, Wang B. Apo A5 21131T/C, FgB 2455G/A, 2148C/T, and CETP TaqIB gene polymorphisms and coronary artery disease in the Chinese population: a meta-analysis of 15,055 subjects. Mol Biol Rep 2013;2013(40):1997–2014 .

      [10] Gu L, Liu W, Yan Y, Su L, Wu G, Liang B, et al. Influence of the b -fibrinogen 455G/A polymorphism on development of ischemic stroke and coronary heart disease. Thrombosis Res 2014;2014 (133):993–1005 .

      [11] Shah PK. Risk factors in coronary artery disease. Boca Raton: Taylor & Francis Group; 2006.

      [12] Granato JE. Living with coronary heart disease: a guide for patients and families. Baltimore: The John Hopkins University Press; 2008 .

      [13] World Health Organization. Global burden of coronary heart disease. Geneva: World Health Organization; 2002 .

      [14] World Health Organization. Cardiovascular disease (CVDs). Geneva: World Health Organization; 2015.

      [15] Ohira T, Iso H. Cardiovascular disease epidemiology in Asia: an overview. Circ J 2013;77:1646–52.

      [16] Chatterjee K. Manual of coronary heart disease. New Delhi: Jaypee Brother Medical Publisher (P) Ltd; 2014 .

      [17] Folsom AR, Wu KK, Rosamond WD, Sharrett AR, Chambless LE. Prospective study of hemostatic factors and incidence of coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) Study. Circulation 1997;96(4):1102–8 .

      [18] Salomaa V, Rasi V, Pekkanen J, Vahtera E, Jauhiainen M, Vartiainen E, et al. Haemostatic factors and prevalent coronary heart disease; The FINRISK haemostasis study. Eur Heart J 1994;15(10):1293–9.

      [19] Zhang Y, Zhu C, Guo Y, Xu R, Li S, Dong Q, et al. Higher fibrinogen level is independently linked with the presence and severity of new-onset coronary atherosclerosis among han chinese population. PLoS One 2014;9(11):e113460.

      [20] Meredith S, Parekh G, Towler J, Schouten J, Davis P, Griffiths H, et al. P87 – M apping nitro-tyrosine modifications in fibrinogen by mass spectrometry as a biomarker for inflammatory disease. Free Radical Biol Med 2014;75(1):S50.

      [21] Sørensen B, Larsen OH, Rea CJ, Tang M, Foley JH, Fenger-Eriksen C. Fibrinogen as a hemostatic agent. Semin Thromb Hemost 2012;38(3):268–73.

      [22] N. Moganarangan, R.G. Babukarthik, S. Bhuvaneswari, M.S. Saleem Basha, P. Dhavachelvan†A Novel Algorithm for Reduction of Energy-consumption in Cloud Computing:Web serviceâ€, Elsevier-journal of king saudi university computer and information’s 28,55-67 (2016).

      [23] R.G. Babukarthik, J. Satheesh Kumar, J. Amudhavel, “Secure data storage and sharing in cloud: vm schedulingâ€, The IIOAB journal (A Journal of Multidisciplinary science and Technology) ISSN: 0976-3104 (Indexed in Emerging Sources Citation Index - Web of Science - Thomson Reuters)" vol:7(7),Pg.1-3.

      [24] “A novel encryption algorithm for end to end secured fiber optic communication", (2017) International Journal of Pure and Applied Mathematics, 117 (19 Special Issue), pp. 269-275.

      [25] Amudhavel, J., Ilamathi, R., Moganarangan, N., Ravishankar, V., Baskaran, R., Premkumar, K., "Performance analysis in cloud auditing: An analysis of the state-of-the-art", (2015) International Journal of Applied Engineering Research, 10 (3), pp. 2043-2046.

      [26] Amudhavel, J., Inbavalli, P., Bhuvaneswari, B., Anandaraj, B., Vengattaraman, T., Premkumar, K., "An effective analysis on harmony search optimization approaches", (2015) International Journal of Applied Engineering Research, 10 (3), pp. 2035-2038.

      [27] “Assessment on authentication mechanisms in distributed system: A case study".

      [28] "Effects, challenges, opportunities and analysis on security based cloud resource virtualization", (2017) Journal of Advanced Research in Dynamical and Control Systems.

      [29] Haripriya, R., "Comprehensive analysis on information dissemination protocols in vehicular ad hoc networks", (2015) International Journal of Applied Engineering Research, 10 (3), pp. 2058-2061.

      [30] Amudhavel, J., "Recursive ant colony optimization routing in wireless mesh network", (2016) Advances in Intelligent Systems and Computing, 381, pp. 341-351.

  • Downloads

  • How to Cite

    Srinivas, V., Aditya, K., Prasanth, G., G.Babukarthik, R., Satheeshkumar, S., & Sambasivam, G. (2018). A Novel Approach for Prediction of Heart Disease: Machine Learning Techniques. International Journal of Engineering & Technology, 7(2.32), 108-110. https://doi.org/10.14419/ijet.v7i2.32.15380