Comparison of the Effects of Cross-validation Methods on Determining Performances of Classifiers Used in Diagnosing Congestive Heart Failure

Open access


Congestive heart failure (CHF) occurs when the heart is unable to provide sufficient pump action to maintain blood flow to meet the needs of the body. Early diagnosis is important since the mortality rate of the patients with CHF is very high. There are different validation methods to measure performances of classifier algorithms designed for this purpose. In this study, k-fold and leave-one-out cross-validation methods were tested for performance measures of five distinct classifiers in the diagnosis of the patients with CHF. Each algorithm was run 100 times and the average and the standard deviation of classifier performances were recorded. As a result, it was observed that average performance was enhanced and the variability of performances was decreased when the number of data sections used in the cross-validation method was increased.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] U.S. National Library of Medicine. Heart failure (Medical Encyclopedia).

  • [2] Flavell C. Stevenson L.W. (2001). Take Heart with Heart Failure. Circulation 104 89.

  • [3] Wilbur J. James P. (2005). Diagnosis and management of heart failure in the outpatient setting. Primary Care 32 1115-1129.

  • [4] American Heart Association (2006). Heart Disease and Stroke Statistics-2006 Update: A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 113 85-151.

  • [5] Isler Y. Kuntalp M. (2007). Combining Classical HRV Indices with Wavelet Entropy Measures Improves to Performance in Diagnosing Congestive Heart Failure. Computers in Biology and Medicine 37(10) 1502-1510.

  • [6] Isler Y. Kuntalp M. (2010). Heart Rate Normalization in the Analysis of Heart Rate Variability in Congestive Heart Failure. Proceedings of the Institution of Mechanical Engineers Part H: Journal of Engineering in Medicine 224(3) 453-463.

  • [7] Yu S.N. Lee M.Y. (2012). Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability. Computers in Biology and Medicine 42 816-825.

  • [8] Yu S.N. Lee M.Y. (2012). Conditional mutual information-based feature selection for congestive heart failure recognition using heart rate variability. Computer Methods and Programs in Biomedicine 108 299-309.

  • [9] Jovic A. Bogunovic N. (2011). Electrocardiogram analysis using a combination of statistical geometric and nonlinear heart rate variability features. Artificial Intelligence in Medicine 51 175-186.

  • [10] Pecchia L. Melillo P. Sansone M. Bracale M. (2011). Discrimination power of short-term heart rate variability measures for CHF assessment. IEEE Transactions on Information Technology in Biomedicine 15(1) 40-46.

  • [11] Narin A. Isler Y. Ozer M. (2014). Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance. Computers in Biology and Medicine 45 72-79.

  • [12] Narin A. Isler Y. (2012). Effect of Principal Component Analysis on Diagnosing Congestive Heart Failure Patients using Heart Rate Records. In IEEE 20th Signal Processing and Communications Applications Conference (SIU2012) 18-20 April 2012 Fethiye / Mugla.

  • [13] Isler Y. Selver M.A. Kuntalp M. (2005). Effects of Detrending in Heart Rate Variability Analysis. In II. Muhendislik Bilimleri Genc Arastirmacilar Kongresi MBGAK’2005 17-19 October 2005 Istanbul 213-219.

  • [14] Duda R.O. Hart P.E. Stork D.G. (2000). Pattern Classification 2nd edition New York. Wiley.

  • [15] Goldberger A.L. (2000). PhysioBank PhysioToolkit and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23) e215-e220.

  • [16] Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. (1996). Heart rate variability: Standards of measurement physiological interpretation and clinical use. Circulation 93 1043-1065.

  • [17] Lomb N.R. (1976). Least-squares frequency analysis of unequally spaced data. Astrophysics and Space Science 39 447-462.

  • [18] Quiraga R.Q. Rosso O.A. Basar E. Schurmann M. (2001). Wavelet entropy in event-related potentials: A new method shows ordering of EEG oscillations. Biological Cybernetics 84(4) 291-299.

  • [19] Woo M.A. Stevenson W.G. Moser D.K. Trelease R.B. Harper R.H. (1992). Patterns of beat-to-beat heart rate variability in advanced heart failure. American Heart Journal 123 704-710.

  • [20] Kamen P.W. Krum H. Tonkin A.M. (1996). Poincare plot of heart rate variability allows quantitative display of parasympathetic nervous activity. Clinical Science 92 201-208.

  • [21] Kamen P.W. (1996). Heart rate variability. Australian Family Physician 25 1087-1094.

  • [22] Brennan M. Palaniswami M. Kamen P. (2001). Do existing measures of poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering 48(11) 1342-1347.

  • [23] Huikuri H.V. Makikallio T.H. Peng C.K. Goldberger A.L. Hintze U. Moller M. (2000). Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. Circulation 101 47-53.

  • [24] Acharya U.R. Kannathal N. Seng O.W. Ping L.Y. Chua T. (2004). Heart rate analysis in normal subjects of various age groups. BioMedical Engineering OnLine 3(24).

  • [25] Caminal P. Vallverdu M. Giraldo B. Benito S. Vazquez G. Voss A. (2005). Optimized symbolic dynamics approach for the analysis of the respiratory pattern. IEEE Transactions on Biomedical Engineering 52(11) 1832-1839.

  • [26] Xu J.H. Liu Z.R. Liu R. (1994). The measures of sequence complexity for EEG studies. Chaos 4(11) 2111-2119.

  • [27] Richman J.S. Randall M.J. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology - Heart and Circulatory Physiology 278 H2039-H2049.

  • [28] Akgul A. (2003). Tibbi Arastirmalarda Istatistiksel Analiz Teknikleri: SPSS Uygulamalari (Statistical Analysis Techniques in Medical Researches: SPSS Experiments). 2nd edition Ankara Turkey Seckin Yayincilik.

  • [29] Kohavi R. (1995). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In 14th International Joint Conference on Artificial Intelligence (IJCAI) 20-25 August 1995 Montreal Quebec Canada.

Journal information
Impact Factor

IMPACT FACTOR 2018: 1.122
5-year IMPACT FACTOR: 1.157

CiteScore 2018: 1.39

SCImago Journal Rank (SJR) 2018: 0.325
Source Normalized Impact per Paper (SNIP) 2018: 0.881

Cited By
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 468 243 3
PDF Downloads 187 95 3