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A Knowledge driven Approach for Efficient Analysis of Heart Disease Dataset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 147 - Number 9 |
| Year of Publication: 2016 |
| Authors: G. N. Beena Bethel, T. V. Rajinikanth, S. Viswanadha Raju |
10.5120/ijca2016911187
|
G. N. Beena Bethel, T. V. Rajinikanth, S. Viswanadha Raju . A Knowledge driven Approach for Efficient Analysis of Heart Disease Dataset. International Journal of Computer Applications. 147, 9 ( Aug 2016), 39-46. DOI=10.5120/ijca2016911187
Abstract
Heart Disease Dataset (HDD) contains high dimensions which poses challenges to research community in terms of complexity and efficient analysis. Heart disease is also called as cardiovascular disease (CVD). Feature selection will be made to reduce the irrelevant and redundant number of attributes. Fast diagnosis of the heart disease can be done using a knowledge driven approach. A comparison was made for medically important features to that of computerized subset of features, to bring out much simpler set of features used for the diagnosis. It focuses on the experts’ judgement for medical driven feature selection process termed as MFS, and the performance of various classifiers on Cleveland dataset for the computerized feature selection termed as CFS and also a combination of both to enhance the prediction accuracy. Further, this paper categorizes the MFS, CFS and the combination of both into discrete and continuous sets of attributes. Our work has proved that the discrete features do not contribute much to the classification as do the continuous ones, in its accuracy, speed and performance.
References
- http://yourtotalhealth.ivallage.com/heart-disease-fast-facts.html, 2008.
- Jesmin Nahar, Tasadduq Imam, Kevin S. Tickle, Yi-Ping Phoebe Chen (2013), “Computational Intelligence for heart disease diagnosis: A medical knowledge driven approach”, Expert system with Applications, 40, 96-104.
- www.indiatoday.intoday.in › India, a report on statistics of causes of death in India 2013.
- Zhao, H., Guo, S., Chen, J., Shi, Q., Wang, J., Zheng, C., et al. (2010). Characteristic pattern study of coronary heart disease with blood stasis syndrome based on decision tree. In 4th international conference on bioinformatics and biomedical engineering (iCBBE) (pp. 1–3). Chengdu, China: IEEE.
- Abraham, R., Simha, J. B., & Iyengar, S. (2007). Medical datamining with a new algorithm for feature selection and Naı¨ve Bayesian classifier. In 10th international conference on information technology, (ICIT), 2007 Orissa IEEE computer society (pp. 44–49).
- Sethi, P., & Jain, M. (2010). A comparative feature selection approach for the prediction of healthcare coverage. Information Systems, Technology and Management, 392–403.
- Polat, K., & Guenes, S. (2009). A new feature selection method on classification of medical datasets: Kernel F-score feature selection. Expert Systems with Applications, 36, 10367–10373.
- Devaney, M., & Ram, A. (1997). Efficient feature selection in conceptual clustering. In Proceedings of the fourteenth international conference on machine learning, Nashville, TN, Citeseer (pp. 92–97).
- Gamboa, A. L. G., Mendoza, M. G., Orozco, R. E. I., VARGAS, J. M., & Gress, N. H. (2006). Hybrid Fuzzy-SV clustering for heart disease identification, computational intelligence for modelling. In International conference on control and automation, 2006 and international conference on intelligent agents, web technologies and internet commerce (pp. 121–121).
- Obayya, M., & Abou-chadi, F. (2008). Data fusion for heart diseases classification using multi-layer feed forward neural network. In International conference on computer engineering & systems, ICCES (Vol. 978, pp. 6–70).
- Maglogiannis, I., Loukis, E., Zafiropoulos, E., & Stasis, S. (2009). Support vectors machine-based identification of heart valve diseases using heart sounds. Computer Methods and Programs in Biomedicine, 95, 47–61.
- K. Usha Rani (2011), “Analysis of Heart Diseases Dataset using Neural Network Approach”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.1, No.5.
- Amma N G B(2012), “Cardiovascular Disease Prediction System using Genetic Algorithm and Neural Network”, International Conference on Computing, Communication and Applications (ICCCA), IEEE Explore.
- Witten, I. H., & Frank, E. (2005). “Data mining: Practical machine learning tools and techniques.” San Francisco: Morgan Kaufmann.
- David W. Aha & Dennis Kibler. "Instance-based prediction of heart-disease presence with the Cleveland database."
- Baliga, R. R., & Eagle, K. A. (2010). “Practical cardiology: Evaluation and treatment of common cardiovascular”. Lippincott Williams & Wilkins.
- Lukasz A. Kurgan and Krzysztof J. Cios, Members of IEEE, (2004), “CAIM Discretization Algorithm”, IEEECS Log Number 114171.
- U. M. Fayyad, K. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proc. of the 12th International Joint Conference on Artificial Intelligence, 1993, pp. 1022-1027.
- Richard Butterworth, Dan A. Simovici, Gustavo S. Santos and Lucila Ohno-Machado, 2004, “A greedy algorithm for supervised discretization”, Elsevier Science.
- James Dougherty, Ron Kohavi, Mehran sehami, “Supervised and unsupervised discretization of continuous features”.
- Cheng-Jung Tsai, Chien-I. Lee, Wei-Pang Yang (2008), “A Discretization algorithm based on Class-Attribute Contingency Coefficient”, Information Sciences, 714–731, Elsevier.
- Ying Yang and Geoffrey I. Webb, “Proportional k-interval discretization for Naive-Bayes Classifiers”, in proceedings of 12 European Conference on Machine Learning (ECML01), pp 564-575.
- H. Liu, F. Hussain, C.L. Tan, M. Dash, (2002), “Discretization: an enabling technique”, Journal of Data Mining and Knowledge Discovery, 393–423.
- Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufman Publishers, 2009.
- Takeharu Hayashi, MD, PHD, Takuro Arimura, DVM, PHD, Manatsu Itoh-Satoh, MD, PHD, Kazuo Ueda, MD, Shigeru Hohda, MD, PHD, Natsuko Inagaki, MD, Megumi Takahashi, MS, Hisae Hori, PHD, Michio Yasunami, MD, PHD, Hirofumi Nishi, MD, PHD, Yoshinori Koga, MD, PHD, Hiroshi Nakamura, MD, PHD, Masunori Matsuzaki, MD, PHD, Bo Yoon Choi, MS, Sung Won Bae, PHD, Cheol Woon You, MD, Kyung Hoon Han, MD, Jeong Euy Park, MD, Ralph Knöll, MD, PHD, Masahiko Hoshijima, MD, PHD, Kenneth R. Chien, MD, PHD, Akinori Kim
- Tommy Jönsson, Yvonne Granfeldt, Bo Ahrén, Ulla-Carin Branell, Gunvor Pålsson, Anita Hansson, Margareta Söderström and Staffan Lindeberg, “Beneficial effects of a Paleolithic diet on cardiovascular risk factors in type 2 diabetes: a randomized cross-over pilot study”, Cardiovascular Diabetology, 2009.
- Valentin Fuster, MD, PHD, FACC, Pedro R. Moreno, MD, FACC, Zahi A. Fayad, PHD, FACC, Roberto Corti, MD, FACC, Juan J. Badimon, PHD, FACC, “Atherothrombosis and High-Risk Plaque Part I: Evolving Concepts”, Vol. 46, No. 6, Journal of the American College of Cardiology, 2005.
- Dariush Mozaffarian, MD, Dr PH; Peter W.F. Wilson, MD; William B. Kannel, MD, MPH, “Beyond Established and Novel Risk Factors Lifestyle Risk Factors for Cardiovascular Disease”, http://circ.ahajournals.org, American Heart Association, 2008.
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