Effective Analysis and Predictive Model of Stroke Disease using Classification Methods
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10.5120/6172-8599 |
A Sudha, P Gayathri and N Jaisankar. Article: Effective Analysis and Predictive Model of Stroke Disease using Classification Methods. International Journal of Computer Applications 43(14):26-31, April 2012. Full text available. BibTeX
@article{key:article,
author = {A. Sudha and P. Gayathri and N. Jaisankar},
title = {Article: Effective Analysis and Predictive Model of Stroke Disease using Classification Methods},
journal = {International Journal of Computer Applications},
year = {2012},
volume = {43},
number = {14},
pages = {26-31},
month = {April},
note = {Full text available}
}
Abstract
In today's world data mining plays a vital role for prediction of diseases in medical industry. Stroke is a lifethreatning disease that has been ranked third leading cause of death in states and in developing countries. The stroke is a leading cause of serious, long term disability in US. The time taken to recover from stroke disease depends on patients' severity. Number of work has been carried out for predicting various diseases by comparing the performance of predictive data mining. Here the classification algorithms like Decision Tree, Naive Bayes and Neural Network is used for predicting the presence of stroke disease with related number of attributes. In our work, principle component analysis algorithm is used for reducing the dimensions and it determines the attributes involving more towards the prediction of stroke disease and predicts whether the patient is suffering from stroke disease or not.
References
- Kohn, L. T. , Corrigan, J. M. , and Donaldson, M. S. , to err is human: building a safer health system. Institute of Medicine (IOM). National Academies Press, Washington, 1999.
- Duen-Yian Yeh a, Ching-Hsue Cheng b, Yen-Wen Chen b A predictive model for cerebrovascular disease using data mining'Science, Vol. 8970-8977, 2011.
- Cheng-Ding Chang a, Chien-Chih Wang b, Bernard C. Jiang Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors Vol 38 ,5507–5513, 2011.
- Genetics and Genomics of Stroke Novel Approaches Alison E. Baird, MBBS, PHD Brooklyn, New York Vol. 56, No. 4, 2010.
- M. Anbarasi et. al. Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm International Journal of Engineering Science and Technology Vol. 2(10), 5370-5376 ,2010.
- ShantakumarB. Patil,Y. S. Kumaraswamy. 'Predictive data mining for medical diagnosis of heart disease prediction'jyoti soni, ujma ansari, dipeshsharma IJCSE Vol . 17, 2011 .
- D. Shanthi,,Dr. G. Sahoo,,Dr. N. Saravanan,2008 'Designing an Artificial Neural Network Model for the Prediction of Thrombo-embolic Stroke (IJBB), Volume 3. pp. 10-18.
- Tamer Uçar a, Adem Karahocaa 'Predicting existence of Mycobacterium tuberculosis on patients using data mining approaches' Vol . 3, 2011.
- Han, J. , Kamber, M. : "Data Mining Concepts and Techniques", Morgan Kaufmann Publishers, 2006.
- Kaur, H. , Wasan, S. K. : "Empirical Study on Applications of Data Mining Techniques in Healthcare", Journal of Computer Science 2(2), 194-200, 2006.
- G. Subbalakshmi et al. Decision Support in Heart Disease Prediction System using Naive Bayes / Indian Journal of Computer Science and Engineering (IJCSE) Vol. 2 No. 2, 2011.
- Shantakumar B. Patil Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network ISSN 1450-216X Vol. 31 No. 4 pp. 642-656, 2009.
- Shantakumar B. Patil and Y. S. Kumaraswamy Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network, European Journal of Scientific Research ISSN 1450- 216X Vol. 31 No. 4, pp. 642-656, 2009.
- American Heart Association. Heart Disease and Stroke Statistics — 2004 Update. Dallas, Tex. : American Heart Association; 2003.
- P. K. Anooj Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules Journal of King Saud University – Computer and Information Sciences 24, 27–40, 2011.
- A. sudha, P Gayathri and N Jaisankar. Utilization of Data mining Approaches for Prediction of Life Threatening Diseases Survivability. International Journal of Computer Applications 41(17):51-55, March 2012.