CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Survey on Diagnosis of Heart Diseases using Data Mining Techniques

by Tashrifa Shahid, Ferdousi Barira
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 22
Year of Publication: 2021
Authors: Tashrifa Shahid, Ferdousi Barira
10.5120/ijca2021921116

Tashrifa Shahid, Ferdousi Barira . A Survey on Diagnosis of Heart Diseases using Data Mining Techniques. International Journal of Computer Applications. 174, 22 ( Feb 2021), 8-12. DOI=10.5120/ijca2021921116

@article{ 10.5120/ijca2021921116,
author = { Tashrifa Shahid, Ferdousi Barira },
title = { A Survey on Diagnosis of Heart Diseases using Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2021 },
volume = { 174 },
number = { 22 },
month = { Feb },
year = { 2021 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number22/31802-2021921116/ },
doi = { 10.5120/ijca2021921116 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:23:49.020823+05:30
%A Tashrifa Shahid
%A Ferdousi Barira
%T A Survey on Diagnosis of Heart Diseases using Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 22
%P 8-12
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining tools are effectively used in disease diagnosis which helps health professional. From health sector a large number of data are collected, classification tools are applied on these data and discover new pattern. In this paper, heart diseases have been chosen for diagnosis and classification. An extensive analysis is performed on some popular data mining methods by using a large number of datasets in this work. To understand the major data mining techniques and select the suitable category of algorithms, the analysis result will help for heart disease analysis. Decision tree has successfully used in different research to predict disease. In this research, decision tree is applied to classify hypertension disease.

References
  1. Asha Rajkumar, Mrs. G.SophiaReena, “Diagnosis Of Heart Disease Using Data mining Algorithm”, Global Journal of Computer Science and Technology, P a g e |38 Vol. 10 Issue 10 Ver. 1.0 September 2010.
  2. Mai Shouman, Tim Turner, Rob Stocker, “Using Decision Tree for Diagnosing Heart Disease Patients”, Proceeding of the 9th Australian Data Mining Conference (AusDM’ 11), Ballarat, Australia.
  3. JyotiSoni, Ujma Ansari, Dipesh Sharma, SunitaSoni, “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction”, International Journal of Computer Applications (0975 – 8887) Volume 17– No.8, March 2011.
  4. Asha Rajkumar, G.SophiaReena, Diagnosis Of Heart Disease Using Data mining Algorithm, Global Journal of Computer Science and Technology 38 Vol. 10 Issue 10 Ver. 1.0 September 2010.
  5. PrachiJambhulkar, VaidehiBaporikar, “Review on Prediction of Heart Disease Using Data Mining Technique with Wireless Sensor Network”, International journal of Computer Science and Application, Vol. 8, No.1, Jan-Mar 2015.
  6. Apte&S.M. Weiss, Data Mining with Decision Tree and Decision Rules, T.J. Watson Research Center, http://www.research.ibm.com/dar/papers/pdf/fgcsap tewe issue_with_cover.pdf,(1997).
  7. K. Thenmozhi, P.Deepika, “Heart Disease Prediction Using Classification with Different Decision Tree Techniques” International Journal of Engineering Research and General Science Volume 2, Issue 6, October-November, 2014.
  8. BoshraBahrami, Mirsaeid Hosseini Shirvani, “Prediction and Diagnosis of Heart Disease by Data Mining Techniques”, Journal of Multidisciplinary Engineering Science and Technology (JMEST) ISSN: 3159-0040 Vol. 2 Issue 2, February – 2015.
  9. Idicula-Thomas, S., Kulkarni, A. J., Kulkarni, B. D.,Jayaraman, V. K., and Balaji, P. V."A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on over expression in escherichiacoli,"Bioinformatics, 2006.
  10. Platt, John, "Sequential minimal optimization: a fast algorithm for training support vector machines,"Technical Report Microsoft Research, 1998.
  11. Chaitrali S. Dangare ,Sulabha S. Apte, “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques”, International Journal of Computer Applications (0975 – 888) Volume 47– No.10, June 2012.
  12. SellappanPalaniappan, RafiahAwang, “Intelligent Heart Disease Prediction System Using Data Mining Techniques”, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8, August 2008.
  13. Mrs.G.Subbalakshmi “Decision Support in Heart Disease Prediction System using Naive Bayes”, Indian Journal of Computer Science and Engineering (IJCSE) ISSN: 0976-5166, Vol. 2 No. 2 Apr-May 2011, pp.170-176.
  14. PrachiJambhulkar, VaidehiBaporikar, “Review on Prediction of Heart Disease Using Data Mining Technique with Wireless Sensor Network”, International Journal of Computer Science And Applications,Vol. 8, No.1, Jan-Mar 2015, ISSN: 0974-1011.
  15. VikasChaurasia and Saurabh Pal “Early Prediction of Heart Diseases Using Data Mining Techniques”, Caribbean Journal of Science and Technology, 2013, ISSN 0799-3757, Vol.1, pp.208-217.
  16. Andreeva, P. “Data Modeling and Specific Rule Generation via Data Mining Techniques”. International Conference on Computer Systems and Technologies - CompSysTech, 2006.
  17. SellappanPalaniappan, RafiahAwang “Intelligent Heart Disease Prediction System Using Data Mining Techniques”,978-1-4244-1968- 5/08/$25.00 ©2008 IEEE.
  18. Sitar-Taut, V.A. “Using machine learning algorithms in cardiovascular disease risk evaluation”, Journal of Applied Computer Science & Mathematics, 2009.
  19. Rajkumar, A. and G.S. Reena “Diagnosis of Heart Disease Using Data mining Algorithm”, Global Journal of Computer Science and Technology, 2010. Vol. 10 (Issue 10).
  20. Srinivas, K., “Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining Techniques”, IEEE Transaction on Computer Science and Education (ICCSE), p(1344 - 1349), 2010.
  21. M. Anbarasi “Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm”, International Journal of Engineering Science and Technology Vol. 2(10),pp.5370-5376, 2010.
  22. KalyaniKadam, AmitaMalav,” A Hybrid Approach for Heart Disease Prediction Using Artificial Neural Network and K-means”, International Journal of Engineering and Technology 2017.
  23. S. Shilna and E. Navya, “ Heart disease forecasting system using k-means clustering algorithm with PSO and other data mining method,” International Journal On Engineering Technology and Sciences ( IJETS), ISSN(P): 2349-3968, ISSN(O): 2349-3976, Vol 3, Issue 4, April 2016.
  24. K.R. Lakshmi, M. V. Krishnaand P Kumar, “Performance Comparison of Data Mining Techniques for Predicting of Heart Disease Survivability”, International Journal of Scientific and Research Publications, ISSN 2250-3153, Vol.3, Issue.6, June 2013.Survivability,”International Journal of Scientific and Research .
  25. K. Solanki, P. Berwal and S. Dalal, “Analysis of application of data mining techniques in healthcare,” International Journal of Computer Applications, August 2016.
  26. Mohammad Bazmara, SaniaVahedianMovahed, Samira Ramadhani, “KNN Algorithm for Consulting Behavioral Disorders in Children”, Journal of Basic and Applied Scientific Research, 2013.
  27. https://www.javatpoint.com/machine-learning-support-vector-machine-algorithm.
  28. Nancy Masih, Sachin Ahuja, “Prediction of Heart Diseases Using Data Mining Techniques: Application on Framingham Heart Study,” International Journal of Big Data and Analytics in Healthcare Volume 3 , Issue 2, July-December 2018.
  29. Jae Kwon Kim, Sanggil Kang, “Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis,” Journal of Healthcare Engineering, Volume 2017.
  30. Mohammad Shafenoor Amin, Yin Kia Chiam, KasturiDewiVarathan, “Identification of significant features and data mining techniques in predicting heart disease,” Telematics and Informatics, Volume 36, March 2019.
  31. KarthikeyanHarimoorthy, MenakadeviThangavelu, “Multi-disease prediction model using improved SVM-radial bias technique in healthcare monitoring system,” Journal of Ambient Intelligence and Humanized Computing, 2020.
Index Terms

Computer Science
Information Sciences

Keywords

Decision Tree Weka tool Naive Bayes Cardiovascular disease SVM

Powered by PhDFocusTM