Analysis and Evaluation of Techniques for Myocardial Infarction Based on Genetic Algorithm and Weight by SVM
محورهای موضوعی : Machine learninghojatallah hamidi 1 , Atefeh Daraei 2
1 - Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
2 - Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
کلید واژه: Artificial Neural Network , Sequential Minimal Optimization , REPTree , Knowledge Discovery in Databases , Myocardial Infarction,
چکیده مقاله :
Although decreasing rate of death in developed countries because of Myocardial Infarction, it is turned to the leading cause of death in developing countries. Data mining approaches can be utilized to predict occurrence of Myocardial Infarction. Because of the side effects of using Angioplasty as main method for diagnosing Myocardial Infarction, presenting a method for diagnosing MI before occurrence seems really important. This study aim to investigate prediction models for Myocardial Infarction, by applying a feature selection model based on Wight by SVM and genetic algorithm. In our proposed method, for improving the performance of classification algorithm, a hybrid feature selection method is applied. At first stage of this method, the features are selected based on their weights, using weight by Support Vector Machine. At second stage, the selected features, are given to genetic algorithm for final selection. After selecting appropriate features, eight classification methods, include Sequential Minimal Optimization, REPTree, Multi-layer Perceptron, Random Forest, K-Nearest Neighbors and Bayesian Network, are applied to predict occurrence of Myocardial Infarction. Finally, the best accuracy of applied classification algorithms, have achieved by Multi-layer Perceptron and Sequential Minimal Optimization.
Although decreasing rate of death in developed countries because of Myocardial Infarction, it is turned to the leading cause of death in developing countries. Data mining approaches can be utilized to predict occurrence of Myocardial Infarction. Because of the side effects of using Angioplasty as main method for diagnosing Myocardial Infarction, presenting a method for diagnosing MI before occurrence seems really important. This study aim to investigate prediction models for Myocardial Infarction, by applying a feature selection model based on Wight by SVM and genetic algorithm. In our proposed method, for improving the performance of classification algorithm, a hybrid feature selection method is applied. At first stage of this method, the features are selected based on their weights, using weight by Support Vector Machine. At second stage, the selected features, are given to genetic algorithm for final selection. After selecting appropriate features, eight classification methods, include Sequential Minimal Optimization, REPTree, Multi-layer Perceptron, Random Forest, K-Nearest Neighbors and Bayesian Network, are applied to predict occurrence of Myocardial Infarction. Finally, the best accuracy of applied classification algorithms, have achieved by Multi-layer Perceptron and Sequential Minimal Optimization.
[1] A.S. Go, D. Mozaffarian, V.L. Roger, E.J. Benjamin, J.D. Berry, W.B. Borden, D.M. Bravata, S. Dai, E.S. Ford, C.S. Fox, and S. Franco."Heart Disease and Stroke Statistics--2013 Update: A Report From the American Heart Association". Circulation, Vol. 127, pp. e6-e245, 2012.
#[2] A. Ahmadi, H. Soori, Y. Mehrabi, K. Etemad, T. Samavat, and A. Khaledifar."Incidence Of Acute Myocardial Infarction In Islamic Republic Of Iran: A Study Using National Registry Data In 2012". Eastern Mediterranean health journal, Vol. 21, pp. 5-12, 2015.
#[3] C. Wiener, C. Brown, A. Hemnes and T. Harrison. Harrison's principles of internal medicine. New York: McGraw-Hill Medical, 2012, pp. 455-456.
#[4] F. Mohammadi, A. Taherian, M. Hosseini and M. Rahgozar. "Effect of Home-Based Cardiac Rehabilitation on Quality of Life in the Patient with Myocardial Infarction". Journal of Rehabilitation, Vol. 7, pp. 11-19, 2006. [In Persian]
#[5] J. E. Hall. Guyton and Hall Textbook of Medical Physiology. New York: Saunders, 2015, pp. 264-266.
#[6] R. Dhingra, J. Shaw and L. A. Kirshenbaum. "molecular regulation of apoptosis signaling pathway in heart" in Apoptosis: Modern Insights into Disease from Molecules to Man, 1st ed., V. R. Preedy, Ed. Florida: CRC Press, 2010, pp. 382-385.
#[7] N. Esfandiari, M. Babavalian, A. Moghadam and V. Tabar. "Knowledge discovery in medicine: Current issue and future trend". Expert Systems with Applications, Vol. 41, pp. 4434-4463, 2014.
#[8] M. Jabbar, B. Deekshatulu and P. Chandra. "Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm". Procedia Technology, Vol. 10, pp. 85-94, 2013.
#[9] S. Kumar and G. Sahoo. "Classification of Heart Disease Using Naïve Bayes and Genetic Algorithm". Computational Intelligence in Data Mining, Vol. 2, pp. 269-282, 2014.
#[10] U. Fayyad and R. Uthurusamy. "Data mining and knowledge discovery in databases". Communications of the ACM, Vol. 39, pp. 24-26, 1996.
#[11] P. Tan, M. Steinbach and V. Kumar. Introduction to data mining. Boston: Pearson Addison Wesley, 2005.
#[12] I. Benjamin, R. C. Griggs, E. J Wing and J. Fitz. Andreoli and Carpenter's Cecil Essentials of Medicine.New York: Saunders, 2015, pp. 93-102.
#[13] W. Baxt, F. Shofer, F. Sites and J. Hollander. "A neural computational aid to the diagnosis of acute Myocardial Infarction". Annals of Emergency Medicine, Vol. 39, pp. 366-373, 2002.
#[14] M. Qazi, G. Fung, S. Krishnan, J. Bi, R. Bharat Rao and A.S. Katz. "Automated heart abnormality detection using sparse linear classifiers". Engineering in Medicine and Biology Magazine, Vol. 26, pp. 56-63, 2007.
#[15] D. Conforti, D. Constanzo and R. Guido. "Medical decision making: A case study within the cardiology domain". Journal on Information Technology in Healthcare, Vol. 5, pp. 343–356, 2007.
#[16] S. Patil and Y. Kumaraswamy. "Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network". European Journal of Scientific Research, Vol. 31, pp. 642-656, 2009.
#[17] M. Karaolis, J. Moutiris, L. Papaconstantinou and C. Pattichis. "Association rule analysis for the assessment of the risk of coronary heart events." in Engineering in Medicine and Biology Society. EMBC 2009. Annual International Conference of the IEEE, 2009, pp. 6238 - 6241.
#[18] M. Arif, I. Malagore and F. Afsar. "Automatic Detection and Localization of Myocardial Infarction Using Back Propagation Neural Networks", in 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), 2010, pp. 2151-7614.
#[19] H. Masethe and M. Masethe, "Prediction of Heart Disease using Classification Algorithm," in Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, 2014.
#[20] N. Krishnaraj and R. Vinothkumar. "Heart Disease Prediction using GA and MLBPN". International Journal of Applied Management & Business Utility, Vol. 2, pp. 17- 24, 2014.
#[21] N. Safdarian, N. Dabanloo and G. Attarodi. "A new pattern recognition method for detection and localization of Myocardial Infarction using t-wave integral and total integral as extracted features from one cycle of ECG signal". Journal of Biomedical Science and Engineering, Vol. 07, pp. 818-824, 2014.
#[22] N. Bhaskar. "Performance analysis of Support Vector Machine and Neural Networks in detection of Myocardial Infarction". Procedia Computer Science, Vol. 46, pp. 20-30, 2015.
#[23] L. Sharma, R. Tripathy and S. Dandapat. "Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction". IEEE Transactions on Biomedical Engineering, Vol. 62, pp. 1827-1837, 2015.
#[24] P. Kora and S. Kalva. "Improved Bat algorithm for the detection of Myocardial Infarction". SpringerPlus, Vol. 4, pp. 1-18, 2015.
#[25] U. Fayyad and R. Uthurusamy. "Data mining and knowledge discovery in databases". Communications of the ACM, Vol. 39, pp. 24-26, 1996.
#[26] N. G. B. Amma. "Cardiovascular disease prediction system using genetic algorithm and Neural Network," in International Conference on Computing, Communication and Applications (ICCCA), 2012, pp. 1-5.
#[27] Han J, Kamber M, Pei J. Data mining: concepts and techniques. Morgan Kaufmann, 2011.
#[28] M. Pacharne and V. Nayak. "Feature Selection Using Various Hybrid Algorithms for Speech Recognition," in Computational Intelligence and Information Technology, 1st ed., V. Das and N. Thankachan, Ed. Berlin: Springer Berlin Heidelberg, 2011, pp. 652-656.
#[29] S. Kalmegh. "Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News". IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 2, pp. 438-446, 2015.
#[30] M. Pal. "Random forest classifier for remote sensing classification". International Journal of Remote Sensing, Vol. 26, pp. 217-222, 2005.
#[31] Y. Wang and J. Vassileva, "Bayesian Network-based trust model." in International Conference on Web Intelligence, IEEE/WIC, 2003, pp.372-378.
#[32] R. Ganesh Kumar and Y. Kumaraswamy. "Performance Analysis Of Soft Computing Techniques For Classifying Cardiac Arrhythmia". Indian Journal of Computer Science and Engineering (IJCSE), Vol. 4, pp. 459-465, 2014.
#[33] M. Gardner and S. Dorling. "Artificial Neural Networks (the multilayer perceptron)—a review of applications in the atmospheric sciences". Atmospheric Environment, Vol. 32, pp. 2627-2636, 1998.
#[34] K. Polat, S. Güneş and A. Arslan. "A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine". Expert Systems with Applications, Vol. 34, pp. 482-487, 2008.
#[35] S. Gunn. "Support Vector Machines for Classification and Regression". University of Southampton, Technical Report, 1998.
#[36] J. Platt. "Sequential minimal optimization:A fast algorithm for training Support Vector Machines". Microsoft Research, Technical report MSR-TR-98-141998.
#[37] D. Hand and K. Yu. "Idiot's Bayes: Not So Stupid after All?", International Statistical Review/Revue Internationale de Statistique, Vol. 69, p. 385, 2001.
#[38] R. Alizadehsani, J. Habibi, B. Bahadorian, H. Mashayekhi, A. Ghandeharioun, R. Boghrati and Z. Alizadeh Sani. "Diagnosis of Coronary Arteries Stenosis Using Data Mining", Journal of Medical Signals and Sensors, Vol. 2, pp. 153-159, 2012.
#[39] A. Onan. "A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer". Expert Systems with Applications, Vol. 42, pp. 6844-6852, 2015.
#[40] M. Heydari, M. Teimouri, Z. Heshmati and S. Alavinia, "Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran", International Journal of Diabetes in Developing Countries, 2015, pp.1-7.
