CLASSIFICATION OF CORONARY ARTERY DISEASE USING HYBRID APPROACH

Auwal Jamilu Abdulsalam, Ayeni K. Bakare, Ahmad I. Baroon, Sulaiman Nura

Abstract


Cardiovascular diseases (CVDs) are known globally to be among the major cause of sudden death, hence the prompt identification of CVDs could help reduce the casualties recorded via them. Diagnosis is a medical term used to describe the “process” involved that lead to the identification of a specific illness. When it comes to Coronary Artery Disease (CAD), however, this is achieved by following sophisticated and costly medical procedures in well-equipped hospitals and healthcare facilities. Furthermore, these procedures usually require only highly qualified medical experts to apply invasive methods. The number of patients who have access to this facility is limited. This research employs the use of Deep Neural Network (DNN) for the diagnosis of CAD for four (4) different datasets with Particle Swarm Optimization (PSO) assisted method for DNN. The aim of this research is to enhance the accuracy of diagnosing heart disease. Developed a conceptual framework to analyze CAD, also integrated PSO training algorithm to train DNN. Finally, evaluate and validate the performance of the proposed hybrid model with benchmark model. The research has shown that PSO is an effective evolutionary computing technique that improves the accuracy of classification. PSO selects the most optimum weight for DNN and increases the classification accuracy. The percentage improvement of the PSO hybridization to DNN are 8.8%, 11.4%, 3.3%, and 11.0% for Cleveland, Hungarian, Switzerland and ValongBeach respectively. The method put forward can improve patient diagnosis reliability and performance as it concerns CAD detection.

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References


Abdullah Caliskan and Mehmet Emin Yuksel “Classification of coronary artery disease data sets by using a deep neural network” Published online: 27 October 2017 DOI:10.24190/ISSN2564-615X/2017/04.03

Awad, N.H., Ali, M.Z., Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound-constrained real-parameter numerical optimization. Technical report, Nanyang Technological University, Singapore, November 2016.

Akata, Z.; Perronnin, F.; Harchaoui, Z.; Schmid, C. Good practice in large-scale learning for image classification. IEEE Trans. Pattern Anal. Mach. Intell. 2014, 36, 507–520. [PubMed]

Anooj PK. Implementing decision tree fuzzy rules in the clinical decision support system after comparing it with fuzzy-based and neural network-based systems. IT Convergence and Security (ICITCS) 2013 International Conference 2013; 1-6.

Baati K, Hamdani TM, Alimi AM. A modified hybrid naive possibilistic classifier for heart disease detection from heterogeneous medical. 2013.

Bounhas M, Mellouli K, Prade H, Serrurier M. Possibilistic classifiers for numerical data. Soft Computing 2012; 17(5): 733-751.

Durairaj. M Sivagowry. S "Feature Diminution by Using Particle Swarm Optimization for Envisaging the Heart Syndrome" I.J. Information Technology and Computer Science, 2015, 02, 35-43 Published Online January 2015 in MECS (http://www.mecs-press.org/)DOI: 10.5815/ijitcs.2015.02

Fukumizu, K.; Bach, F.R.; Jordan, M.I. Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces. J. Mach. Learn. Res. 2004, 5, 73–99.

Geman, S.; Bienenstock, E.; Doursat, R. Neural networks and the bias/variance dilemma. Neural Netw. 2008, 4, 1–58.

Gilles Louppe “U N D E R S TA N D I N G R A N D O M F O R E S T S from theory to practice” University of Liège Faculty of Applied Sciences Department of Electrical Engineering & Computer Science PhD dissertation 2014 G.E. Hinton, S. Osindero, Y.W. Tec, A fast learning algorithm for deep belief nets, Neural Comput. 18 (7) (2006) 1527–1554.

Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997)

Mohammad Reza Daliri “Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis” DOI 10.1515/bmt-2012-0009

Michal Pluhacek1(&), Roman Senkerik1, Adam Viktorin1, Tomas Kadavy1, and Ivan Zelinka A Review of Real-World Applications of Particle Swarm Optimization Algorithm 2Springer International Publishing AG 2018.

Nahar J, Imam T, Tickle KS, Chen YPP. Computational intelligence for heart disease diagnosis: A medical knowledge-driven approach. Expert systems with Applications. 2013; 40(1): 96-104.

N. Ghadiri Hedeshi and M. Saniee Abadeh, Research Article, “Coronary Artery Disease Detection Using a Fuzzy-Boosting PSO Approach” Computational Intelligence and Neuroscience Volume 2014, Article ID 783734, 12 pages http://dx.doi.org/10.1155/2ele014/783734

Nisbet Robert,... Gary Miner,” Learn more about Data Redundancy” in Handbook of Statistical Analysis and Data Mining Applications, 2009

Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11(4), 3658–3670 (2011). ISSN 1568-4946

Nettleton, D.F.; Orriols-Puig, A.; Fornells, A. A study of the effect of different types of noise on the precision of supervised learning techniques. Artif. Intell. Rev. 2010, 33, 275–306.

Paul D. Allison, Convergence Failures in Logistic Regression University of Pennsylvania, Philadelphia, PA Paper 360-2008

R.O. Duda and P.E. Hart. Pattern classification and scene analysis. New York: John Wiley and Sons, 1973.

Raducanu, B.; Dornaika, F. A supervised non-linear dimensionality reduction approach for manifold learning. Pattern Recognit. 2012, 45, 2432–2444.

Shi, Y.H., Eberhart, R.C.: Empirical Study of Particle Swarm Optimization. IEEE Congress on Evolutionary Computation (1999)

Salman, A., Ahmad, I.: Particle Swarm Optimization for Task Assignment Problem. Microprocessors and Microsystems. 26 (2002) 363-371

Srinivas, K., B.Kavihta, R. & Govardhan, A., 2010. Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks. International Journal on Computer Science and Engineering (IJCSE), March, 02(02), pp. 250-255.

Srishti Taneja. (2014). Implementation of the Novel Algorithm (SPruning Algorithm). IOSR Journal of Computer Engineering (IOSR-JCE), 57-65.

Unler, A.; Murat, A.; Chinnam, R.B. MR 2 PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification. Inf. Sci. 2011, 181, 4625–4641. [CrossRef]

Uma N Dulhare, “Prediction system for heart disease using Naive Bayes and particle swarm optimization.” Biomedical Research 2018; 29 (12): 2646-2649.

World Health Organization; 2018, Global Health Estimates 2016: Deaths by Cause, Age, Sex by country and by region, 2000-2016. Geneva

Zawbaa Hossam Mona Nagy Elbedwehy, et. al., “Binary PSO -KNN-SVM Diagnosing heart diseases Detection of Heart Disease using Binary Particle Swarm Optimization”, 2012.


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