Diagnosis of hyperlipidemia in patients based on an artificial neural network with pso algorithm
الموضوعات :asma naeimi 1 , minoo soltanshahi 2 , amir rajabi 3
1 - Lecturer
2 - Lecturer
3 - stu
الکلمات المفتاحية: prognosis, neural network algorithm pso, data mining, cardiovascular disease, Hyperlipidemia,
ملخص المقالة :
One of the most common and most dangerous diseases of blood fats are such as heart disease, diabetes and stroke, heart and brain. It can control the timely diagnosis, treatment and then prevention of complications is become very effective even without using medicine. Heart disease and diabetes file if patients has useful information that can be used to estimate blood fat timely diagnosis. In this paper we introduce a method based on data mining according to the information of patients' medical records to predict and detect blood lipid cardiovascular. And to identify patients with high blood lipids,we use a category based on neural network without feedback and pso algorithm to train the neural network to determine the appropriate value to reduce error the weights of the neural network . Simulation is done in MATLAB environment by using Body Fat data set, it shows the accuracy of 93.22 percent compared to the same methods, which means high accurate, higher detection sensitivity and Democrats.
[1] Rahimi shateranlo, E. And Alizadeh, S., 2014. Predict coronary heart disease using a combination of data mining models. Iranian conference: soft computing and IT 2014, Volume-3 Issue-1
[2] Safdari, R., Ghazi.s, M., Gharooni, M., Nasiri, M. And Arji, G.,2014. Compare the performance of decision trees and neural network in the prediction of myocardial infarction. Journal of Mashhad Medical Sciences and Rehabilitation,Volume-3 Issue-1
[3] Zamanpoor, S. And Shamsi, M., 2012. Comparative evaluation of the accuracy of data mining algorithms to predict heart disease, Fourth Conference Electrical and Electronic Engineering,iran-gonabad.
[4] Kashefi.k, A., Pormousa, A., and jahanbani, A.,2007. Multi-layer neural network training using the PSO algorithm, Eighth Conference Intelligent Systems,mashhad-iran.
[5] Crawford M., 2009. Current diagnosis & treatment in cardiology 2009. 3rd ed. Newyork: mcgraw-Hill Medical.
[6] Mobley, B. A., Schechter, E., Moore, W. E., mckee, P. A., and Eichner, J. E. (2005). Neural network predictions of significant coronary artery stenosis in men. Artificial intelligence in medicine, 34(2), 151-161.
[7] Nahar, J., Imam, T., Tickle, K. S., and Chen, Y. P. P. (2013). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40(4), 1086-1093.
[8] Bennetts, C. J., Owings, T. M., Erdemir, A., Botek, G. And Cavanagh, P. R. (2013). Clustering and classification of regional peak plantar pressures of diabetic feet. Journal of biomechanics, 46(1), 19-25.
[9] Canivell, S. And Gomis, R. (2014). Diagnosis and classification of autoimmune diabetes mellitus. Autoimmunity reviews, 13(4), 403-407.
[10] Ordon, M., Urbach, D., Mamdani, M., Saskin, R., Honey, R. J. D. A. And Pace, K. T. (2014). The surgical management of kidney stone disease: a population based time series analysis. The Journal of urology, 192(5), 1450-1456.
[11] Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A. And Havel, J. (2013). Artificial neural networks in medical diagnosis. Journal of applied biomedicine, 11(2), 47-58.
[12] Santhanam, T. And Padmavathi, M. S. (2015). Application of K-Means and Genetic Algorithms for Dimension Reduction by Integrating SVM for Diabetes Diagnosis. Procedia Computer Science, 47, 76-83.
[13] López-Chau, A., Cervantes, J., López-García, L. And Lamont, F. G. (2013). Fisher’s decision tree. Expert Systems with Applications, 40(16), 6283-6291.
[14] Lappenschaar, M., Hommersom, A., Lucas, P. J., Lagro, J. And Visscher, S. (2013). Multilevel Bayesian networks for the analysis of hierarchical health care data. Artificial intelligence in medicine, 57(3), 171-183.
[15] Han, J., Kamber, M. And Pei, J. (2011). Data mining: concepts and techniques: concepts and techniques. Www.Elsevier.com
[16] Ezanjani, H. Introduction to data mining, www.hajarian.com/IT/tahghigh/zanjani.pdf
[17] Rezai, A., Keshavarzi, P., and Mahdiye, R. (2014). A novel MLP network implementation in CMOL technology. Engineering Science and Technology, an International Journal, 17(3), 165-172.
[18] Wang, C., Li, L., Wang, L., Ping, Z., Flory, M. T., Wang, G., and Li, W. (2013). Evaluating the risk of type 2 diabetes mellitus using artificial neural network: An effective classification approach. Diabetes research and clinical practice, 100(1), 111-118.
[19] Saritha, M., Joseph, K. P., & Mathew, A. T. (2013). Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recognition Letters, 34(16), 2151-2156.
[20] Jalalian, A., Mashohor, S. B., Mahmud, H. R., Saripan, M. I. B., Ramli, A. R. B., & Karasfi, B. (2013). Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clinical imaging, 37(3), 420-426.
[21] Bala, S., & Kumar, K. (2014). A Literature Review on Kidney Disease Prediction using Data Mining Classification Technique.
[22] Bajaj, P., Choudhary, K., &Chauhan, R. (2015). Prediction of Occurrence of Heart Disease and Its Dependability on RCT Using Data Mining Techniques. Ininformation Systems Design and Intelligent Applications (pp. 851-858). Springer India.
[23] Suykens, J. A. And Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300.
[24] Basak, D., Pal, S. And Patranabis, D. C. (2007). Support vector regression.Neural Information Processing-Letters and Reviews, 11(10), 203-224.
[25] Fadini, G. P. And Avogaro, A. (2013). Diabetes impairs mobilization of stem cells for the treatment of cardiovascular disease: a meta-regression analysis. International journal of cardiology, 168(2), 892-897.
[26] D'Ascenzo, F., Agostoni, P., Abbate, A., Castagno, D., Lipinski, M. J., Vetrovec, G. W., ... And Gaita, F. (2013). Atherosclerotic coronary plaque regression and the risk of adverse cardiovascular events: a meta-regression of randomized clinical trials. Atherosclerosis, 226(1), 178-185.
[27] Soni, J., Ansari, U., and Shrma, D. 2010. Intelligent and Effective Heart Disease Prediction System using Weighted Associative Classifiers, IJCSE.
[28] Mohammadpour Tahamtan, A., Esmaeili, M., Ghaemian, A. And Esmaeili.J.2012. Application of Artificial Neural Network for Assessing Coronary Artery Disease,J Mazand Univ Med Sci, 2012, 22(86) 9-17.
[29] Jyoti, S., Ujma, A., Dipesh, S. And Sunita, S. 2011. Predictive Data Mining for Medical Diagnosis. An Overview of Heart Disease Prediction, International Journal of Computer Applications 2011, 17(8): 35-43.
[30] Biglarian, A., Babaee, R. And Azmie, R. 2004. Application of Artificial Neural Network Model in Determining Important Predictors of In Hospital Mortality After Coronary Artery Bypass Graft Surgery, and it’s Comparison with Logistic Regression Model ,Modarres J Med Sci 2004, 7(1), 23-30. [Persian]
[31] Colombet, I., Ruelland, A., Chatellier, G., Gueyffier F., Degoulet, P. And Christine, M. 2000. Models to predict cardiovascular risk: comparison of CART, Multilayer perception and logistic regression. Proc AMIA Symp 2000:156-160.
[32] Dubey, A., Patel, R. And Choure, K. 2014. An Efficient Data Mining and Ant Colony Optimization technique (DMACO) for Heart Disease Prediction, International Journal of Advanced Technology and Engineering Exploration, Volume-1 Issue-1 December-2014.
[33] Fadini, G. P. And Avogaro, A. (2013). Diabetes impairs mobilization of stem cells for the treatment of cardiovascular disease: a meta-regression analysis. International journal of cardiology, 168(2), 892-897.
[34] Chau, K.W. and Cheng, C.T., 2002. Real-time prediction of water stage with artificial neural network approach. Lecture Notes in Artificial Intelligence 2557, 715.
[35] Rumelhart, D.E., Hinton, E. And Williams, J., 1986. Learning internal representation by error propagation. Parallel Distributed Processing 1, 318–362.
[36] Bazartseren, B., Hildebrandt, G., Holz, K.-P., 2003. Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing 55 (3–4), 439–450.
[37] Haykin, S., 1999. Neural Networks, A Comprehensive Foundation. Prentice Hall, Upper Saddle River.
[38] Rogers, L.L., Dowla, F.U. and Johnson, V.M., 1995. Optimal field-scale groundwater remediation using neural networks and the genetic algorithm. Environmental Science and Technology 29 (5), 1145– 1155.
[39] Rumelhart, D.E., Hinton, E. And Williams, J., 1986. Learning internal representation by error propagation. Parallel Distributed Processing 1, 318– 362.
[40] Clerc, M. And Kennedy, J., 2002. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. EEE Transactions on Evolutionary Computation 6 (1), 58–73.
[41] Konstantinos E. Parsopoulos and Michael N. Vrahatis, 2004. On the Computation of All Global Minimizers Through Particle Swarm Optimization, IEEE transactions on evolutionary computation, vol. 8, no. 3, june 2004
