Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

November Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the November 2021 Edition of the journal. The last date of research paper submission is October 15, 2021.

Comparative Analysis of Accuracy on Heart Disease Prediction using Classification Methods

Rovina Dbritto, Anuradha Srinivasaraghavan, Vincy Joseph. Published in Biomedical

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Rovina Dbritto, Anuradha Srinivasaraghavan, Vincy Joseph
10.5120/ijais2016451578
Download full text
  1. Rovina Dbritto, Anuradha Srinivasaraghavan and Vincy Joseph. Comparative Analysis of Accuracy on Heart Disease Prediction using Classification Methods. International Journal of Applied Information Systems 11(2):22-25, July 2016. URL, DOI BibTeX

    @article{10.5120/ijais2016451578,
    	author = "Rovina Dbritto and Anuradha Srinivasaraghavan and Vincy Joseph",
    	title = "Comparative Analysis of Accuracy on Heart Disease Prediction using Classification Methods",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "July 2016",
    	volume = 11,
    	number = 2,
    	month = "Jul",
    	year = 2016,
    	issn = "2249-0868",
    	pages = "22-25",
    	numpages = 4,
    	url = "http://www.ijais.org/archives/volume11/number2/908-2016451578",
    	doi = "10.5120/ijais2016451578",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

A common term heart disease is nothing but a cardiovascular disease or a Coronary heart disease which reduces the efficiency and proper functioning of heart by blocking veins, artery or blood vessels around it. Coronary heart disease causes disability such as damage to the brain resulting in death. Based on Statistics [10] it indicates that range of age group from 25 to 69 have 25% risk of having heart diseases. Some vital causes for cardiovascular disease are, physical inactivity, smoking, consuming more junk food and addiction of alcohol which are major causes for stroke, chest pain, and heart attack. However because of the awareness about factors and symptoms that are responsible for heart problem, it is possible to predict any heart problem based on statistical analysis of medical records. However Data mining, a modern technique has provided an automatic way of analyzing data using standard classification methods. Though many classifiers are available in data mining that can be used to predict the heart problems, this paper emphasizes on finding the appropriate classifier that has the potential to give better accuracy by applying data mining techniques viz. Naïve Bayes , Support Vector machine and Logistic Regression.

Reference

  1. Minas A. Karaolis, Joseph A. Moutiris, Demetra Hadjipanayi, Constantinos S. Pattichis,” Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees”, IEEE Transactions On Information Technology In Biomedicine, VOL. 14, NO. 3, MAY 2010.
  2. Raghunath Nambiar, Adhiraaj Sethi, Ruchie Bhardwaj, Rajesh Vargheese,” A Look at Challenges and Opportunities of Big Data Analytics in Healthcare”, 2013 IEEE International Conference on Big Data.
  3. T.John Peter, K. Somasundaram,” An Empirical Study on Prediction of Heart Disease Using Classification Data Mining Techniques”, IEEE, International conference on Advances in engineering, science and management,pp.514-518, 2012.
  4. Eman AbuKhousa, Piers Campbell,” Predictive Data Mining to Support Clinical Decisions: An Overview of Heart Disease Prediction Systems”, IEEE, International Conference on Innovations in Information Technology, pp.267-272, 2012.
  5. Aqueel Ahmed, Shaikh Abdul Hannan,” Data Mining Techniques to Find Out Heart Diseases: An Overview”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2012.
  6. Lamia AbedNoor Muhammed,” Using Data Mining technique to diagnosis heart disease”, IEEE, International conference on statistics in science, Buiseness and Engineering, pp.1-3, 2012.
  7. Sivagowry S, Dr. Durairaj. M and Persia. A & Research Scholar, “An Empirical Study on Applying Data Mining Techniques for the Analysis and Prediction Heart Disease”, IEEE, International Conference on Information Communication and embedded system, pp.265-270, 2013.
  8. M.Akhil jabbar , Dr.Priti Chandra, Dr.B.L Deekshatulu,” Heart Disease Prediction System Using Associative Classification and Genetic Algorithm”, ICECIT, 2012.
  9. Ranganatha S., Pooja Raj H.R., Anusha C., Vinay S.K.,” Medical Data Mining And Analysis For Heart Disease Dataset Using Classification Techniques”,IEEE, National conference on challenges in research and technology in the coming decades,pp.1-5,2013.
  10. Vikas Chaurasia, Saurabh Pal, “Early Prediction of Heart Diseases Using Data Mining Techniques”, Carib.j.SciTech, 2013, Vol.1, 208-217.
  11. Mamuna Fatima, Iqra Basharat, Dr. Shoab Ahmed Khan, Ali Raza Anjum,, “Biomedical (Cardiac) Data Mining: Extraction of significant patterns for predicting heart condition”, IEEE conference on Computational Intelligence in bioinformatics and computational biology, pp.1-7, 2014.
  12. Mythili T., Dev Mukherji, Nikita Padalia, and Abhiram Naidu “A Heart Disease Prediction Model using SVM- Decision Trees-Logistic Regression (SDL)”, IJCA, Vol.68- No.16 April 2013.
  13. Carlos O., Edward O , Levien de Braal, and team “Mining Constrained Association Rules to Predict Heart Disease”, IEEE, International Conference on Data Mining p.433-440, 2001.

Keywords

Coronary, Naive Bayes, Support Vector Machine, Logistic Regression