Predictive Analysis using Data Mining Techniques for Heart Disease Diagnosis

  • Authors

    • Siddharth Joshi
    • Ashish Sasanapuri
    • Shreyash Anand
    • Saurav Nandi
    • Varsha Nemade
    2018-08-04
    https://doi.org/10.14419/ijet.v7i3.1.17079
  • Data mining, heart diseases, predictive Analysis.

  • Due to technological advancements in the field of computer science and data warehousing techniques. The healthcare industry ranging from small clinics to large hospital campuses use Content management system which has made the storage and accessing of data a faster option. But these large amounts of data generated are regrettably not mined and the data remains unexploited. Through this research we aim to demonstrate the use of Data Mining algorithm by using python programming language in order to create a desktop-based application which will cater to our aim. This Paper will analyze the performance by comparing the metrics of data analysis like accuracy, precision and recall in order introducing our software solution which tries to be more accurate than the work previously done on Cleveland, VA Hungarian data sets taken from UCI repository [1].

     

     

  • References

    1. [1] Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science

      [2] Rajkumar, Asha, and G. Sophia Reena. "Diagnosis of heart disease using datamining algorithm." Global journal of computer science and technology 10.10 (2010): 38-43.

      [3] Kelley, Deeanna. "Heart disease: Causes, prevention, and current research." JCCC Honors Journal 5.2 (2014):

      [4] Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.,University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. , University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. , V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:Robert Detrano, M.D., Ph.D.

      [5] Sajja, Sunitha. "Data mining of medical datasets with missing attributes from different sources." PhD diss., Youngstown State University, 2010.

      [6] Rajput, Anupama, M. Ramachandran, V. D. Gotmare, and P. P. Raichurkar. "Recent Bioactive Materials for Development of Eco-friendly Dippers: An Overview." Journal of Pharmaceutical Sciences and Research 9, no. 10 (2017): 1844-1848.

      [7] Fayyad, Piatetsky-Shapiro, Smyth, "From Data Mining to Knowledge Discovery: An Overview",in Fayyad, Piatetsky-Shapiro, Smyth, Uthurusamy, Advances in Knowledge Discovery and Data Mining, AAAI Press / The MIT Press, Menlo Park, CA, 1996, pp.1-34

      [8] BabiÄ, FrantiÅ¡ek, et al. "Predictive and descriptive analysis for heart disease diagnosis." Computer Science and Information Systems (FedCSIS), 2017 Federated Conference on. IEEE, 2017.

      [9] L. Verma, S. Srivastaa, and P.C. Negi, "A Hybrid Data Mining Model to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data", Journal of Medical Systems, vol. 40, no. 178, 2016, doi: 10.1007/s10916-016-0536-z.

      [10] R. El-Bialy, M. A. Salamay, O. H. Karam, and M. E. Khalifa, "Feature Analysis of Coronary Artery Heart Disease Data Sets", Procedia Computer Science, ICCMIT 2015, vol. 65, pp. 459–468, doi: 10.1016/j.procs.2015.09.132

      [11] Cheung, Marian C., et al. "Antioxidant supplements block the response of HDL to simvastatin-niacin therapy in patients with coronary artery disease and low HDL." Arteriosclerosis, thrombosis, and vascular biology 21.8 (2001): 1320-1326.

      [12] Yan, Wei-wu, and Hui-he Shao. "Application of support vector machines and least squares support vector machines to heart disease diagnoses." Control and Decision 18.3 (2003): 358-360.

      [13] Andreeva, Plamena. "Data modelling and specific rule generation via data mining techniques." International Conference on Computer Systems and Technologies-CompSysTech. 2006.

      [14] Sitar-Taut, D. A., et al. "Using machine learning algorithms in cardiovascular disease risk evaluation." Journal of Applied Computer Science & Mathematics 3.5 (2009): 29-32

      [15] Deepali Mor, M Ramachandran, Pramod Raichurkar, “Optimization of Solid Wastes Disposal Strategy by Fuzzy Topsis Methodâ€, Nature Environment and Pollution Technology 16(1):247-250, 2017.

      [16] Shouman, Mai, Tim Turner, and Rob Stocker. "Using data mining techniques in heart disease diagnosis and treatment." Electronics, Communications and Computers (JEC-ECC), 2012 Japan-Egypt Conference on. IEEE, 2012.

      [17] BabiÄ, FrantiÅ¡ek, et al. "On patient’s characteristics extraction for metabolic syndrome diagnosis: Predictive modelling based on machine learning." International Conference on Information Technology in Bio-and Medical Informatics. Springer, Cham, 2014.

      [18] Siddharth. J et al. Int. Journal of Engineering Research and Application ISSN : 2248-9622, Vol. 7, Issue 4, ( Part -2) April 2017, pp.14-19

  • Downloads

  • How to Cite

    Joshi, S., Sasanapuri, A., Anand, S., Nandi, S., & Nemade, V. (2018). Predictive Analysis using Data Mining Techniques for Heart Disease Diagnosis. International Journal of Engineering & Technology, 7(3.1), 166-170. https://doi.org/10.14419/ijet.v7i3.1.17079