Prediction of Heart Diseases Using Data Mining and Machine Learning Algorithms and Tools

Authors

  • M. Nikhil Kumar  Department of CSE, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India
  • K. V. S. Koushik  Department of CSE, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India
  • K. Deepak  Department of CSE, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India

Keywords:

Data Mining, Machine Learning, Decision Tree, Heart Disease

Abstract

Data mining is the most popular knowledge extraction method for knowledge discovery (KDD). Machine learning is used to enable a program to analyze data, understand correlations and make use of insights to solve problems and/or enrich data and for prediction. Data mining techniques and machine learning algorithms play a very important role in medical area. The health care industry contains a huge amount of data. But most of it is not effectively used. Heart disease is one of the main reason for death of people in the world. Nearly 47% of all deaths are caused by heart diseases. We use 8 algorithms including Decision Tree, J48 algorithm, Logistic model tree algorithm, Random Forest algorithm, Naïve Bayes, KNN, Support Vector Machine, Nearest Neighbour to predict the heart diseases. Accuracy of the prediction level is high when using more number of attributes. Our aim is to perform predictive analysis using these data mining, machine learning algorithms on heart diseases and analyze the various mining, Machine Learning algorithms used and conclude which techniques are effective and efficient.

References

  1. Prerana T H M1, Shivaprakash N C2 , Swetha N3 "Prediction of Heart Disease Using Machine Learning Algorithms- Naïve Bayes,Introduction to PAC Algorithm, Comparison of Algorithms and HDPS" International Journal of Science and Engineering Volume 3, Number 2 – 2015 PP: 90-99 ©IJSE Available at www.ijse.org ISSN: 2347-2200
  2. B.L Deekshatulua Priti Chandra "Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm " M.Akhil jabbar* International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA) 2013.
  3. Michael W.Berry et.al,"Lecture notes in data mining",World Scientific(2006)
  4. S. Shilaskar and A. Ghatol, "Feature selection for medical diagnosis : Evaluation for cardiovascular diseases," Expert Syst. Appl., vol. 40, no. 10, pp. 4146–4153, Aug. 2013. 
  5. C.-L. Chang and C.-H. Chen, "Applying decision tree and neural network to increase quality of dermatologic diagnosis," Expert Syst. Appl., vol. 36, no. 2, Part 2, pp. 4035–4041, Mar. 2009. 
  6. A. T. Azar and S. M. El-Metwally, "Decision tree classifiers for automated medical diagnosis," Neural Comput. Appl., vol. 23, no. 7–8, pp. 2387–2403, Dec. 2013. 10Y. C. T. Bo Jin, "Support vector machines with genetic fuzzy feature transformation for biomedical data classification.," Inf Sci, vol. 177, no. 2, pp. 476–489, 2007.
  7. N. Esfandiari, M. R. Babavalian, A.-M. E. Moghadam, and V. K. Tabar, "Knowledge discovery in medicine: Current issue and future trend," Expert Syst. Appl., vol. 41, no. 9, pp. 4434–4463, Jul. 2014.
  8. A. E. Hassanien and T. Kim, "Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks," J. Appl. Log., vol. 10, no. 4, pp. 277–284, Dec. 2012.
  9. Sanjay Kumar Sen 1, Dr. Sujata Dash 21Asst. Professor, Orissa Engineering College, Bhubaneswar, Odisha – India.
  10. UCI Machine Learning Repository, Available at http://archive.ics.uci.edu/ml/machinelearningdatabases/ statlog/german/
  11. Domingos P and Pazzani M. "Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier", in Proceedings of the 13th Conference on Machine Learning, Bari, Italy, pp105-112, 1996.
  12. Elkan C. "Naive Bayesian Learning, Technical Report CS97-557", Department of Computer Science and Engineering, University of California, San Diego, USA, 1997.
  13. B.L Deekshatulua Priti Chandra "Reader, PG Dept. Of Computer Application North Orissa University, Baripada, Odisha – India. "Empirical Evaluation of Classifiers’ Performance Using Data Mining Algorithm" International Journal of C omputer Trends and Technology (IJCTT) – Volume 21 Number 3 – Mar 2015 ISSN: 2231-2803 http://www.ijcttjournal.org Page 146

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

Downloads

Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
M. Nikhil Kumar, K. V. S. Koushik, K. Deepak, " Prediction of Heart Diseases Using Data Mining and Machine Learning Algorithms and Tools, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.887-898, March-April-2018.