Feature Selection Using Particle Swarm Optimization for Predicting the Risk of Cardiovascular Disease in Type-II Diabetic Patients

Authors

  • Radha P Ph. D Scholar, Dept. Of Computer Science, Karpagam University, Coimbatore and Asst. Prof, Dept. Of Computer Science, Vellalar College for Women, Thindal, Erode
  • Srinivasan B Associate Professor, Dept. Of Computer Science, Gobi Arts and Science College, Gobichettiplayam

Keywords:

Classification, Hybrid Prediction Model, Fuzzy c means clustering(FCM), Principal Component Analysis (PCA), Kullback Leiber Divergence(KLD), Extreme Learning Machine (ELM), Fast Correlation-Based Filter Solution(FCBFS), Particle Swarm Optimization (PSO)

Abstract

Diabetes is the most common disease nowadays in all populations and in all age groups. A wide range of computational methods and tools for data analysis are available to predict the T2D patients with CVD risk factors. Efficient predictive modelling is required for medical researchers and practitioners to improve the prediction accuracy of the classification methods .The aim of this research was to identify significant factors influencing type 2 diabetes control with CVD risk factors, by applying particle swarm optimization feature selection system to improve prediction accuracy and knowledge discovery. Proposed system consists of four major steps such as preprocessing and dimensionality reduction of type 2 diabetes with CVD factors, Attribute Value Measurement, Feature Selection, and Hybrid Prediction Model. In proposed methods the pre-processing and dimensionality reduction of the patients records is performed by using Kullback Leiber Divergence(KLD) Principal component analysis (PCA), then attribute values measurement is performed using Fast Correlation-Based Filter Solution(FCBFS), feature selection is performed by using Particle Swarm Optimization (PSO), finally hybrid prediction model which uses Improved Fuzzy C Means (IFCM) clustering algorithm aimed at validating chosen class label of given data and subsequently applying Extreme Learning Machine(ELM) classification algorithm to the result set.

References

. Dr Alan Rees, ―Excess cardiovascular risk in patients with type 2 diabetes: do we need to look beyond LDL cholesterol?‖, Br J Diabetes Vasc Dis 2014;14:10-20.

. Engelgau MM, Geiss LS, Saaddine JB, Boyle JP, Benjamin SM, Gregg EW, Tierney EF, RiosBurrows N, Mokdad AH, Ford ES, Imperatore G, Narayan KM: The evolving diabetes burden in the United States. Ann Intern Med 140: 945–950, 2004.

. Hu FB, Stampfer MJ, Solomon CG, Liu S, Willett WC, Speizer FE, Nathan DM, Manson JE: The impact of diabetes mellitus on mortality from all causes and coronary heart disease in women: 20 years of follow-up. Arch Intern Med 161: 1717–1723, 2001

. Fox CS, Coady S, Sorlie PD, Levy D, Meigs JB, D’Agostino RB Sr, Wilson PW, Savage PJ: Trends in cardiovascular complications of diabetes. JAMA 292: 2495–2499, 2004.

. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, Marks JS: Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 289:76–79, 2003

. Thomas F, Bean K, Pannier B, Oppert JM, Guize L, Benetos A: Cardiovascular mortality in overweight subjects: the key role of associated risk factors. Hypertension 46:654–659, 2005.

. Brunner EJ, Shipley MJ, Witte DR, Fuller JH, Marmot MG: Relation between blood glucose and coronary mortality over 33 years in the Whitehall Study. Diabetes Care 29:26–31, 2006.

. Yusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, Commerford P, Lang CC, Rumboldt Z, Onen CL, Lisheng L, Tanomsup S, Wangai P Jr, Razak F, Sharma AM, Anand SS, the INTERHEART Study Investigators: Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case control study. Lancet 366:1640–1649, 2005.

. Guthrie RA, Guthrie DW, editors. Nursing management of diabetes mellitus. 5th ed., New York: Springer Publishing; 2002.

. Assmann G, Cullen P, Schulte H: Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Munster (PROCAM) study. Circulation 105:310–315, 2002.

. Eberly LE, Prineas R, Cohen JD, Vazquez G, Zhi X, Neaton JD, Kuller LH, the Multiple Risk Factor Intervention Trial Research Group: Metabolic syndrome: risk factor distribution and 18-year mortality in the Multiple Risk Factor Intervention Trial. Diabetes Care 29:123–130, 2006

. Wilson PW, D’Agostino RB, Parise H, Sullivan L, Meigs JB: Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation 112:3066–3072, 2005.

. Crone S, Lessmann S, Stahlbock R. Empirical comparison and evaluation of classifier performance for data mining in customer relationship management. In: Wunsch D, et al., editors. Proceedings of the international joint conference on neural networks, IJCNN’04. 2004. p. 443—8.

. Kantardzic M, editor. Data mining: concepts, models, methods, and algorithms. New Jersey: Wiley-IEEE Press; 2002.

. Su CT, Yang CH, Hsu KH, Chiu WK. Data mining for the diagnosis for type II diabetes from three-dimensional body surface anthropometrical scanning data. Comput Math Appl 2006;51:1075—92.

. Huang Y, McCullagh PJ, Black ND. Feature selection via supervised model construction. In: Bramer M, editor. Proceedings of the 4th IEEE international conference on data mining. 2004. p. 411—4.

. Robnik M, Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 2003;53:23—69.

. Myatt, G. J. (2007). Making sense of data a practical guide to exploratory data analysis and data mining. New Jersey: John Wiley & Sons.

. Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques (2nd ed.). Morgan Kaufmann Publishers.

. A. M. Martinez and A. C. Kak, ―PCA versus LDA,‖ IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2,

pp. 228–233, 2001

. Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multi-class classification. IEEE Trans Syst

Man Cybern 42(2):513–529

. K. Crammer, and Y. Singer. On the learn ability and design of output codes for multiclass problems. Machine Learning. 2002, 47 (2-3): 201- 233

. Pahikkala T, Tsivtsivadze E, Airola A, Boberg J, Salakoski T (2007) Learning to rank with pairwise regularized least-squares. In: Joachims T, Li H, Liu TY, Zhai C (eds) Proceedings of the SIGIR 2007workshop on learning to rank for information retrieval. ACM, Amsterdam, Netherlands, pp 27–33.

. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research (JAIR), 16, 321–357.

. P. Radha, Dr. B. Srinivasan : Diagnoing a. Heart Diseases for Type 2 Diabetic Patients by b. cascading the Data Mining Techniques.

. International Journal on Recent and Innovation

. Trends in Computing and Communication

. (IJRITCC) vol 2, issue 8, Aug 2014, 2503-2509.

Downloads

Published

2024-02-26

How to Cite

Radha, P., & Srinivasan, B. (2024). Feature Selection Using Particle Swarm Optimization for Predicting the Risk of Cardiovascular Disease in Type-II Diabetic Patients. COMPUSOFT: An International Journal of Advanced Computer Technology, 3(11), 1327–1336. Retrieved from https://ijact.in/index.php/j/article/view/230

Issue

Section

Original Research Article

Similar Articles

<< < 3 4 5 6 7 8 9 10 11 12 > >> 

You may also start an advanced similarity search for this article.