Heart Disease Prediction and Treatment Using KNN Algorithm

Authors

  • Narasimhulu M  Master of Computer Applications, RCR Institute of Management & Technology, Tirupati, Andhra Pradesh, India
  • Somasekhar K  Head of the Department, Master Of Computer Applications, RCR Institute Of Management & Technology, Tirupati, Andhra Pradesh, India

Keywords:

Data Mining, Heart Disease Diagnosis and Treatment

Abstract

The accessibility of immense measures of restorative information prompts the requirement for capable information investigation instruments to separate helpful learning. Specialists have for quite some time been worried about applying factual and information mining instruments to enhance information examination on huge informational collections. Infection analysis is one of the applications where information-mining devices are demonstrating victories. Coronary illness is the main source of death everywhere throughout the world in the previous ten years. A few specialists are utilizing factual and information mining devices to enable wellbeing to mind experts in the conclusion of coronary illness. Utilizing single information mining procedure in the determination of coronary illness has been extensively explored indicating satisfactory levels of precision. As of late, analysts have been exploring the impact of hybridizing in excess of one method demonstrating upgraded brings about the conclusion of coronary illness. In any case, utilizing information-mining procedures to distinguish a reasonable treatment for coronary illness patients has gotten less consideration. This paper distinguishes holes in the exploration on coronary illness analysis and treatment and proposes a model to efficiently close those holes to find if applying information-mining systems to coronary illness treatment information can give as dependable execution as that accomplished in diagnosing coronary illness.

References

  1. Johnston G. System adds to biodefense readiness. Bio-IT World. November 1, 2002. Available at www.bio-itworld.com/ news/110102_report1436.html.Accessed July 21, 2004.
  2. Matkovsky IP, Nauta KR. Overview of data mining techniques. Presented at the Federal Database Colloquium and Exposition; September 9-11, 1998; San Diego, CA.
  3. Lajiness MS. Using Enterprise Miner to explore and exploit drug discovery data. Proceedings from the 25th Annual SAS User Group International; April 9-12, 2000; Indianapolis, IN.
  4. Perleman RS, Smith KM. Novel software tools for chemical diversity. Perspectives in Drug Discovery & Design 1998;9:339-353.
  5. Mostashari F, Kulldorff M, Hartman J, Miller J, Kulasekera V. Dead bird clusters as an early warning system for West Nile virus activity. Emerg Infect Dis 2003;9:641-646.
  6. Gaynes R, Richards C, Edwards J, et al. Feeding back surveillance data to prevent hospital-acquired infections. Emerg Infect Dis 2001;7:295-298. 15. Brosette SE, Spragre AP, Jones WT, Moser SA. A data mining system for infection control surveillance. Methods Inf Med 2000;39:303-310.
  7. Cerrito P. Using text analysis to examine ICD-9 codes to determine uniformity in the reporting of MedPAR data. Presented at the Annual Symposium of the American Medical Informatics Association; November 9-13, 2002; San Antonio, TX.
  8. Ridinger M. American Healthways uses SAS to improve patient care. DM Review 2002;12:139.
  9. M. Kumari, S. Godara, “Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction,” International Journal of Computer Science and Technology, vol. 2, Issue 2, June (2011).[10] N. Al-Milli, “Backpropagation neural network for prediction of heart disease,” Journal of theoretical and applied information Technology, vol. 56, pp.131-135, Oct 10, 2013.
  10. R. Das, I. Turkoglu, A. Sengur, Effective Diagnosis of Heart Disease through Neural Network Ensemble, “Expert Systems with Applications” vol. 36 issue 4, pp. 7675- 7680, May (2009). [Available]: 10.1016/j.eswa.2008.09.013.
  11. N. Guru, A. Dahiya and N. Rajpal, “Decision Support System for Heart Disease Using Neural Network,” Delhi Business Review, vol. 8, No 1, pp. 1 – 6, Jan – June (2007).
  12. E. O.Olaniyi, K. Adnan., “Onset Diabetes Diagnosis Using Artificial Neural Network”, International Journal of Scientific and Engineering Research, vol. 5, Issue 10, October (2014).
  13. N. Kilic, B. Ekici, S. Hartomacioglu, “Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools,” Defence Technology xx pp. 1-13, 2015. [online]: http://dx.doi.org/10.1016/j.dt.2014.12.001.
  14. A. K. Nanila, A. P. Singh, “Fault Diagnosis of MixedSignal Analog Circuit Using Artificial Neural Network”, International Journal of Intelligent Systems and Applications, 07, pp. 11-17, 2015. DOI: 10.5815/ijisa.2015.07.02.
  15. S. Bhuvaneswari, J. Sabarathinam, “Defect Analysis Using Artificial Neural Network” I.J. Intelligent Systems and Applications, 05, pp. 33-38, 2013.
  16. S. Moein, S. A. Monadjemi and P. Moallem, "A Novel Fuzzy-Neural Based Medical Diagnosis System", International Journal of Biological & Medical Sciences, Vol.4, No.3, 2009, pp. 146-150.
  17. P. S. Heckerling, G. Canaris, S. D. Flach, T. G. Tape, R. S. Wigton and B. S. Gerber, "Predictors of urinary tract infection based on artificial neural networks and genetic algorithms", International Journal of Medical Informatics; Vol.76, No.4, 2007, pp. 289-296.
  18. M. P. Francisco, G. C. Juan Manuel, S. P. Antonio and R. F. Daniel, "A robust model of the neuronal regulator of the lower urinary tract based on artificial neural networks", Neurocomputing, Vol.71, No.4-6, 2008, pp. 743-754.
  19. S. A. Monadjemi and P. Moallem, "Automatic Diagnosis of Particular Diseases Using a Fuzzy-Neural Approach", International Review on Computers & Software, Vol.3, No.4, 2008, pp. 406-411.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-30

Issue

Volume 3, Issue 4, March-April-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Narasimhulu M, Somasekhar K, " Heart Disease Prediction and Treatment Using KNN Algorithm, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 4, pp.1056-1059, March-April-2018.