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A Hybrid Method for Coronary Heart Disease Risk Prediction using Decision Tree and Multi Layer Perceptron


  • Department of Computer Science, Vellalar College for Women, Thindal, Erode - 638012, Tamil Nadu, India
  • Electrical Sciences, Sri Krishna College of Technology, Kovaipudur, Coimbatore - 641042, Tamil Nadu, India


Background/Objectives: The diagnosis of Coronary Heart Disease (CHD) risk prediction is a vital and complicated job in medicine which is closely linked with lifestyle related behaviors. The main intention of this work is to build up a rapid and automatic prediction of CHD risk by integrating Decision Tree (DT) and Multi Layer Perceptron (MLP). Methods/Statistical Analysis: The proposed hybrid method consists of two stages in which risk identification is carried out in the first stage and level prediction is done in the second stage. In the first stage the physical and biochemical factors are classified using C4.5 algorithm in DT. In the second stage the CHD risk identified instances from DT are analyzed using MLP with habitation and medical history attributes. Findings: The obtained classification accuracies of this system are 98.66% for DT and 96.66% for MLP. The performance analysis of the proposed method is evaluated using sensitivity and specificity which helps to reduce the healthcare costs, further invasive CHD risk examination and waiting time of the individuals. Application/Improvements: The proposed work structured an optimal predictive tool for diagnosing CHD risk, which can further serve as an implication sketch for physicians in clinical diagnosis.


Coronary Heart Disease, Data Mining, Decision Tree, 10-Fold Cross Validation, Multi Layer Perceptron

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  • Polat K, Gunes S. A hybrid approach to medical decision support systems: Combining feture selection, fuzzy weightes pre-processing and AIRS. Computer Methods and Programming in Biomedicine. 2007 Nov; 88(2):164–74.
  • Tsipouras MG, Exarchos TP, Fotiadas, Kotsia AP, Vakalis KV, Naka KK, Michalis LK. Automated diagnosis of Coronary Artery Disease based on data mining and fuzzy mod-eling. IEEE Transactions on Information Technology in Biomedicine. 2008 Jul; 12(4):447–58.
  • Muthukaruppan S, Er MJ. A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Expert Systems with Applications. 2012 Oct; 39(14):11657–65.
  • Srinivas K. Analysis of Coronary Heart Disease and prediction of heart attack in coal mining regions using data mining techniques. Proceedings of 5th ICCSE: Hefei China;2010 Aug 24-27. p. 1344–9.
  • Yanwei X, Jie W, Zhihong Z, Yonghong G. Combination data mining methods with new medical data to predicting outcome of coronary heart disease. Proceedings of ICCIT:Gyeongju Republic of Korea; 2007 Nov 21-23. p. 868–72.
  • Matsumori R, Miyazaki T, Shimada K, Kume A, Kitamura Y, Oshida K, Yanagisawa N, Kiyanagi T, Hiki M, Fukao K, Hirose K, Ohsaka H, Mokuno H, Daida H. High levels of very long-chain saturated fatty acid in erythrocytes correlates with atherogenic lipoprotein profiles in subjects with metabolic syndrome. Diabetes research and clinical practice.2013 Jan; 99(1):12–8.
  • 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; 111(1):52–61.
  • Podgorelec V, Kokol P, Stiglic B, Rozman I. Decision trees:an overview and their use in medicine. Journal of Medical Systems. 2002 Oct; 26(5):445–63.
  • Bigert C, Gustavsson P, Hallqvist J, Hogstedt C, Lewne M, Plato N, Reuterwall C, Scheele P. Myocardial Infarction among professional driver. Epidemiology. 2003 May;14(3):333–9.
  • Ragland DR, Krause N, Greiner BA, Fisher JM. Studies of health outcomes in transit operators: policy implications of the current scientific database. Journal of Occupational Health Psychology. 1998 Apr; 3(2):172–87.
  • Claire HQ, Jen MN, Troy HP. Does occupational driving increase the risk of cardiovascular disease in people with diabetes? Diabetes Research and Clinical Practice. 2013 Jan;99(1):e9–e11.
  • Prevention of Cardio Vascular Disease pocket guidelines for assessment and management of cardio vascular risk. 2015. Available from: cardiovascular_diseases/guidelines/PocketGL.ENGLISH.AFR-D-E.rev1.pdf
  • Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Pidetcha P, Prachayasittikul V. Identification of metabolic syndrome using decision tree analysis. Diabetes Research and Clinical Practice. 2010 Oct;90(1):e15–e18.
  • Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods.Artificial Intelligence in Medicine. 2005 Jun; 34(2):113–27.
  • Shannon CE. A mathematical theory of communication. Bell System Technical Journal. 1948 Jul; 27(3):379–423.
  • Kukar M, Kononenko I, Groselj C, Krali K, Fettich J. Analyzing and improving the diagnosis of ishaemic heart disease with machine learning. Artificial Intelligence in Medicine.1999 May; 16(1):25–50.


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