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Reseach Article

Classification and Prediction Model using Hybrid Technique for Medical Datasets

by Raghavendra S., Indiramma M.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 5
Year of Publication: 2015
Authors: Raghavendra S., Indiramma M.
10.5120/ijca2015906382

Raghavendra S., Indiramma M. . Classification and Prediction Model using Hybrid Technique for Medical Datasets. International Journal of Computer Applications. 127, 5 ( October 2015), 20-25. DOI=10.5120/ijca2015906382

@article{ 10.5120/ijca2015906382,
author = { Raghavendra S., Indiramma M. },
title = { Classification and Prediction Model using Hybrid Technique for Medical Datasets },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 5 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number5/22725-2015906382/ },
doi = { 10.5120/ijca2015906382 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:19:05.954644+05:30
%A Raghavendra S.
%A Indiramma M.
%T Classification and Prediction Model using Hybrid Technique for Medical Datasets
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 5
%P 20-25
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For processing of large amount of data numerous techniques are used. Data Mining is one of the technique that is used most often. To process these data, Data mining combines traditional data analysis with sophisticated algorithms. Medical data mining is an important area of Data Mining and considered as one of the important research field due to its application in healthcare domain. Classification and prediction of medical datasets poses real challenges in Medical Data Mining. To cope with these challenges Logistic Regression (LR) and Artificial Neural Network (ANN) are commonly used. LR enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. LR explains that there can be one or more independent variables that can determine the problem outcome. ANN resembles the human brain and here the information is processed by simple elements called neurons and signals are transmitted between the neurons. Feature subset selection selects subsets of features that are enough to explain the target concept. In this paper feature selection methods like forward selection and backward elimination using mean evaluation are used on the medical datasets. LR and ANN are applied on feature selection methods using Cross Validation Sample (CVS) and Percentage Split as test options. From the experimental results it is identified that for SPECTF dataset LR using percentage split prediction accuracy of 83.95% is achieved, for Diabetes Dataset LR using percentage split prediction accuracy of 80.46% is achieved, and for Liver Disorder dataset NN using percentage split prediction accuracy of 74.75% is achieved.

References
  1. Sunita Soni, Ujma Ansari, Dipesh Sharma and Jyoti Soni,        “Predictive Data Mining for Medical Diagnosis: An        Overview of Heart Disease Prediction”, International        Journal of Computer application (0975-8887), vol. 17,        no.8, March (2011).
  2. Raghavendra B.K., Jay B. Simha, “Performance Evaluation of Logistic Regression and Neural Network Model with Feature Selection Methods and Sensitivity Analysis on Medical Data Mining”, International Journal of Advanced Engineering Technology (Vol. II, Issue: I, January-March 2011), pp. 288-298.
  3. Raghavendra B.K., S.K. Srivatsa, Raghavendra S, Shivashankar S.K., “Comparison of Logistic Regression and Neural Network Model with and without hidden Layers”, Universal Journal of Applied Computer Science and Technology, Vol.1, 2011, pp. 49-53.
  4. Qi Cheng, Pramod K. Varshney, and Manoj K. Arora, Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data”, Geoscience  and Remote Sensing Letters, IEEE, Vol. 3, No. 4, pp. 491-494.
  5. Raghavendra B.K., Jay B. Simha, “Evaluation of Logistic Regression Model with Feature Selection on Medical Dataset”, International Journal of Computational Intelligence (Vol.1, Issue 2, Dec 2010), pp. 35-42.
  6. Qinbao Song, Jingjje Ni and Guangtao Wang, “A Fast Clustering Based Feature Subset Selection Algorithm for High Dimensional Data”, IEEE Transactions on Knowledge and data engineering 2013, Vol 25, Issue 1, pp 1-14.
  7. Amin S.U., Agarwal, K. and Beg, R.,” Genetic neural network based data mining in prediction of heart disease using risk factors”, IEEE Conference on        Information & Communication Technologies (ICT), Page(s):1227– 1231, 2013.
  8. Kumari, Sonu Singh and Archana, ”A data mining approach for the diagnosis of diabetes mellitus“, 7th International Conference on Intelligent Systems and Control (ISCO), Page(s): 373 – 375, January 2013.
  9. Rachata N., Charoenkwan P., Yooyativong T. Chamnongthal K., Lursinsap C. and Higuchi, K.” Automatic Prediction System of Dengue Haemorrhagic Fever Outbreak Risk by Using Entropy and Artificial Neural Network“, International Symposium on Communications and Information        Technologies (ISCIT), Page(s): 210 – 214, October 2008.
  10. Sandya Joshi, Deepa Shenoy, Vibhudendra Simha G.G., P. L. Rashmi, and K. R. Venugopal, “Classification of Alzheimer’s Disease and Parkinson’s Disease by Using Machine Learning and Neural Network Method”, Second International Conference on Machine Learning and Computing, page(s): 218- 222, 2010.
  11. T. John Peter, and K. Somasundaram, “An Empirical Study on Prediction of Heart Disease Using Classification Data Mining Techniques”, IEEE International Conference On Advances In Engineering.
  12. R.Robu and C. Hora”, “Medical Data Mining with Extended WEKA”, IEEE International Conference on Intelligent Engineering System (INES 2012), June 2012, page(s): 347-350.
  13. Ankita Dewan and Meghna Sharma, ”Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classification”, 2015, page(s): 704-706.
  14. Sikander Singh Khuri, and Gurpreet Singh, “Ranking Early Signs of Coronary Heart Disease Among Indian Patients”, 2015, page(s): 840-844.
  15. Sana Shaikh, Amit sawant, Shreerang Paradkar, Kedar Patil, “Electronic Recording System-Heart Disease Prediction System”, International Conference on Technologies for Sustainable Development (ICTSD 2015), February 2015.
  16. Raghavendra S, and Indiramma M., ”Performance Evaluation of Logistic Regression and Artificial Neural Network Model with Feature Selection Methods using Cross Validation Sample and Percentage Split on Medical Datasets”, Second International Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA- 2014), August-2014.
Index Terms

Computer Science
Information Sciences

Keywords

Cross Validation Sample Data Mining Mean Evaluation Feature Subset Selection Logistic Regression Artificial Neural Network Percentage Split.