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A Data Mining Model for Coronary Artery Disease Detection Using Noninvasive Clinical Parameters
Coronary Artery Disease (CAD) is one of the major cause of death as well as disability worldwide. Suspected cases of CAD go through invasive and non-invasive tests to get CAD detected. Angiography is a noninvasive method for detection. Not only it is costly, time consuming and risky, but it also needs technical expertise and not suitable for the screening of large population. Hence researchers are looking for better alternatives using non-invasive clinical tests. Objectives: To construct Artificial Neural Network based model for CAD identification and adjudging its accuracy with respect to other models. Method: Data mining techniques are being employed to identify CAD cases based on non-invasive clinical tests. Early detection of disease is necessary in order to avoid the risk being exaggerated further. Benchmark Cleave land heart disease data is collected from UCI machine repository and Probabilistic Neural Network is employed and trained and tested using non-invasive clinical parameters. Finding: Neural Network based model is presented that uses non-invasive clinical parameters of the subjects to model CAD cases and achieves the diagnosis accuracy of 96% and misclassification error rate of 4%.The models’ performance is also compared with other classifiers such as RBF Network, AD Tree. Improvement: Neural network based model showed the superiority over other methods in terms of accuracy. Results are promising and reproducible and therefore the model can be valuable adjunct tool in clinical practices.
Coronary Artery Disease, Data Mining, Decision Tree, Probabilistic Neural Network.
- Han J, Pei J, Kamber M. Data mining: concepts and techniques.3rd edn.Elsevier; 2011.
- Gupta M, Dahiya D. Performance evaluation of classification algorithms on different data sets. Indian Journal of Science and Technology. 2016 Oct; 9(40):1–6.
- Meenakshi M, Geetika G. Survey on classification methods using WEKA. International Journal of Computer Applications.2014 Jan; 86(18):16–19.
- Verma A, Gill A, Kaur I. Analysis and implementation of data mining algorithms for deploying ID3, CHAID and Naive Bayes for random dataset. Indian Journal of Science and Technology. 2016Oct; 9(40):1–32.
- Verma TR, DeeptiG. Implementation of clustering algorithms in rapidminer. IFRSA International Journal of Data Warehousing and Mining. 2014Feb; 4(1):59–61.
- Verma A, Kaur I, Singh I. Comparative Analysis of Data Mining Tools and Techniques for Information Retrieval.Indian Journal of Science and Technology. 2016 Mar; 9(11).
- Aggarwal N, Gaur D. Classification of crime data using rapid miner. International Journal of Applied Engineering Research. 2015; 10(35).
- Chhikara RR, Sharma P, Singh L. A hybrid feature selection approach based on improved PSO and filter approaches for image steganalysis. International Journal of Machine Learning and Cybernetics. 2016 Dec; 7(6):1195–206.
- Zhonglin T, Xueping N. Application of data mining in university research management system. Proceedings of 4th IEEE international conference on Computational and Information Sciences (ICCIS), China; 2012 Aug 17. p. 761–3.
- Ghosh S, Nag A, Biswas D, Singh JP, Biswas S, Sarkar D, Sarkar PP. Weather data mining using artificial neural network.Proceedings of IEEE Conference on Recent Advances in Intelligent Computational Systems (RAICS), India; 2011 Sep. p. 192–5.
- Zamani Z, Pourmand M, Saraee MH. Application of data mining in traffic management: case of city of Isfahan. Proceedings of IEE international conference on Electronic Computer Technology (ICECT); 2010. p. 102–6
- Da Cunha C, Agard B, Kusiak A. Data mining for improvement of product quality. International Journal of Production Research. 2006 Sep 15;44(18–19):4027–41.
- Ahmed SR. Applications of data mining in retail business.Proceedings of IEEE International Conference on Information Technology: Coding and Computing (ITCC), 2004 Apr 5–7, Nevada. 2004;2:455–9.
- Brossette SE, Sprague AP, Hardin JM, Waites KB, Jones WT, Moser SA. Association rules and data mining in hospital infection control and public health surveillance. Journal of the American Medical Informatics Association. 1998 Jul 1;5(4):373–81.
- Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: Towards better research applications and clinical care. Nature Reviews Genetics. 2012 Jun 1;13(6):395–405.
- Aljumah AA, Ahamad MG, Siddiqui MK. Application of data mining: Diabetes health care in young and old patients.Journal of King Saud University-Computer and Information Sciences. 2013 Jul 31;25(2):127–36.
- Verma L, Srivastava S, Negi PC. A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. Journal of Medical Systems. 2016 Jul 1;40(7):1–7.
- Kumar V, Verma L. Binary classifiers for health care databases: A comparative study of data mining classification algorithms in the diagnosis of breast cancer. International Journal of Computer Science and Technology. 2010 Dec; 1(2):124–9.
- Available from:http://www.who.int/mediacentre/factsheets/ fs317/en/ Date accessed: 01/01/2016
- Acharya UR, Sree SV, Krishnan MM, Krishnananda N, Ranjan S, Umesh P, Suri JS. Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images. Computer Methods and Programs in Biomedicine. 2013 Dec 31; 112(3):624–32.
- Escolar E, Weigold G, Fuisz A, Weissman NJ. New imaging techniques for diagnosing coronary artery disease. Canadian Medical Association Journal. 2006 Feb; 174(4):487–95.
- Giri D, Acharya UR, Martis RJ, Sree SV, Lim TC, Ahamed T, Suri JS. Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowledge-based Systems. 2013 Jan 31;37:274– 82.
- Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, Boghrati R, Ghandeharioun A, Bahadorian B, Sani ZA.A data mining approach for diagnosis of coronary artery disease. Computer Methods and Programs in Biomedicine.2013 Jul 31;111(1):52–61.
- Kahramanli H, Allahverdi N. Design of a hybrid system for the diabetes and heart diseases. Expert Systems with Applications.2008 Aug 31;35(1):82–9.
- Polat K, Şahan S, Güneş S. Automatic detection of heart disease using an Artificial Immune Recognition System (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing. Expert Systems with Applications. 2007 Feb 28;32(2):625–31.
- Das R, Turkoglu I, Sengur A. Diagnosis of valvular heart disease through neural networks ensembles. Computer Methods and Programs in Biomedicine. 2009 Feb 28;93(2):185–91.
- Specht DF. Probabilistic neural networks. Neural Networks.1990 Jan 1;3(1):109–18.
- Kusy M, Zajdel R. Probabilistic neural network training procedure based on Q (0)-learning algorithm in medical data classification. Applied Intelligence. 2014 Oct 1;41(3):837–54.
- Freund Y, Mason L. The alternating decision tree learning algorithm. Inicml. 1999 Jun 27; 99;124–33.
- Fu X, Wang L. Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2003 Jun;33(3):399–409.
- Bouali H, Akaichi J. Comparative study of different classification techniques: Heart disease use case. Proceedings of 13th IEEE international conference on Machine Learning and Applications (ICMLA), Detroit; 2014 Dec 3. p. 482–6.
- Tu MC, Shin D, Shin D. Effective diagnosis of heart disease through bagging approach. Proceedings of IEE2nd International Conference on Biomedical Engineering and Informatics, 2009 Oct 17, China; 2009. p. 1–4.
- Peter TJ, Somasundaram K. An empirical study on prediction of heart disease using classification data mining techniques. Proceedings of IEEE international conference on Advances in Engineering, Science and Management (ICAESM), India; 2012 Mar 30. p. 514–18.
- Kahramanli H, Allahverdi N. Design of a hybrid system for the diabetes and heart diseases. ExpertSystems with Applications.2008 Aug 31;35(1):82–9.
- Palaniappan S, Awang R. Intelligent heart disease prediction system using data mining techniques. Proceedings of 12th IEEE/ACS International Conference on Computer Systems and Applications, Egypt; 2008 Mar. p. 108–15.
- El Bialy R, Salama MA, Karam O. An ensemble model for heart disease data sets: a generalized model. Proceedings of the ACM 10th International Conference on Informatics and Systems, Egypt;2016 May. p. 191–6.
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