INFORMATION CHANGE THE WORLD

International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.6, No.6, Nov. 2016

Utilization of Data Mining Classification Approach for Disease Prediction: A Survey

Full Text (PDF, 239KB), PP.45-52


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Author(s)

Divya Jain, Vijendra Singh

Index Terms

Data Mining;Classification algorithms;Disease prediction;Healthcare Sector

Abstract

Early diagnosis of a disease is a vital task in medical informatics. Data mining is one of the principal contributors in this discipline. Utilization of Data Mining Technology in Disease Forecasting System is a recognized trend and is successfully emerging in this domain. In today`s world, Heart Disease is the one of the most prevalent disease among people with a high mortality rate. It is essential to classify the reports of heart patients into correct subclasses to lower fatality rate. Over the years, Data mining classification and prediction approaches has been used extensively for disease prediction. This paper comes out with the compilation, analysis as well as comparative study of numerous classification approaches used for predictive analysis of several diseases. The goal of the survey is to provide a comprehensive review of the work done on disease prediction using different classification approaches in data mining.

Cite This Paper

Divya Jain, Vijendra Singh,"Utilization of Data Mining Classification Approach for Disease Prediction: A Survey", International Journal of Education and Management Engineering(IJEME), Vol.6, No.6, pp.45-52, 2016.DOI: 10.5815/ijeme.2016.06.05

Reference

[1]Ian H. Witten and Eibe Frank, "Data Mining: Practical machine learning tools and techniques". Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2nd edition.

[2]D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining. ISBN 0-471-66657-2, John Wiley & Sons, Inc., 2005.

[3]P.N. Tan, M Steinbach, V. Kumar, Introduction to Data Mining. 4th edn. Pearson Publications, Boston.

[4]J. Han, M. Kamber, Data Mining: Concepts And Techniques. Morgan Kaufmann, San Francisco (2001).

[5]M. H. Dunham, S. Sridhar, Data Mining: Introductory and Advanced Topics, Pearson Education, New Delhi, ISBN: 81-7758-785-4, 1st Edition, 2006.

[6]S. Palaniappan., R. Awang, "Intelligent Heart Disease Prediction System Using Data Mining Techniques", IJCSNS International Journal of Computer Science and Network Security 8(8) (August 2008).

[7]S. Vijayarani, S. Dhayanand ,"Data Mining Classification Algorithms for Kidney Disease Prediction", International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 4, August 2015 DOI: 10.5121/ijci.2015.4402 13.

[8]V. Krishnaiah, G. Narsimhaand N. Subhash Chandra, "Heart Disease Prediction System Using Data Mining Technique by Fuzzy K-NN Approach", Emerging ICT for Bridging the Future – Volume 1,Advances in Intelligent Systems and Computing 337, DOI: 10.1007/978-3-319-13728-5_42.

[9]AH Chen, SY Huang, PS Hong, CH Cheng, EJ Lin,"HDPS: Heart Disease Prediction System", Computing in Cardiology 2011;38:557-560.

[10]Purushottam, Kanak Saxena and Richa Sharma, "Efficient Heart Disease Prediction System using Decision Tree", International Conference on Computing, Communication and Automation (ICCCA2015).

[11]M. A. Nishara Banu, B. Gomathy ,"Disease Forecasting System Using Data Mining Methods", 2014 International Conference on Intelligent Computing Applications.

[12]Sujata Joshi and Mydhili K. Nair, "Prediction of Heart Disease Using Classification Based Data Mining Techniques", Computational Intelligence in Data Mining - Volume 2, Smart Innovation, Systems and Technologies 32, DOI 10.1007/978-81-322-2208-8_46.

[13]Dana AL-Dlaeen and Abdallah Alashqur, "Using Decision Tree Classification to Assist in the Prediction of Alzheimer's Disease", 2014 6th International Conference on CSIT ISBN:987-1-4799-3999-2.

[14]Monika Gandhi and Shailendra Narayan Singh, "Predictions in Heart Disease Using Techniques of Data Mining", 2015 1st International Conference on Futuristic trend in Computational Analysis and Knowledge Management (ABLAZE-2015).

[15]Sana Shaikh, Amit Sawant, Shreerang Paradkar and Kedar Patil, "Electronic Recording System - Heart Disease Prediction System", 2015 International Conference on Technologies for Sustainable Development (ICTSD-2015), Feb. 04 – 06, 2015, Mumbai, India.

[16]Purushottam, Kanak Saxena and Richa Sharma, "Diabetes Mellitus Prediction System Evaluation Using C4.5 Rules and Partial Tree", 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions).

[17]Hlaudi Daniel Masethe, Mosima Anna Masethe, "Prediction of Heart Disease using Classification Algorithms", Proceedings of the World Congress on Engineering and Computer Science 2014 Vol II WCECS 2014, 22-24 October, 2014, San Francisco, USA.

[18]Jyoti Soni, Uzma Ansari and Dipesh Sharma, "Intelligent and Effective Heart Disease Prediction System using Weighted Associative Classifiers", International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 6 June 2011.

[19]Rucha Shinde, Sandhya Arjun, Priyanka Patil and Jaishree Waghmare "An Intelligent Heart Disease Prediction System Using K-Means Clustering and Naïve Bayes Algorithm",(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (1) , 2015, 637-639.

[20]S. Vijayarani and S.Dhayanand ,"Kidney Disease Prediction Using Svm And Ann Algorithms", International Journal of Computing and Business Research (IJCBR), ISSN (Online) : 2229-6166, Volume 6 Issue 2 March 2015.

[21]Rashedur M. Rahman and Farhana Afroz , "Comparison Of Various Classification Techniques Using Different Data Mining Tools For Diabetes Diagnosis", Journal of Software Engineering and Applications, 2013, 6, 85-97.

[22]Ritika Chadha, Shubhankar Mayank, Anurag Vardhan and Tribikram Pradhan, "Application of Data Mining Techniques on Heart Disease Prediction: A Survey", Emerging Research in Computing, Information, Communication and Applications, DOI 10.1007/978-81-322-2553-9_38.