# Congestive Heart Failure Identification on Normal vs Congestive Heart Failure (CHF) Source: [Congestive Heart Failure Identification on Normal vs Congestive Heart Failure (CHF)]() Collection index: [llms.txt]() Data updated: 2026-02-26T05:54:23.162Z Evaluation of model performance for identifying Congestive Heart Failure (CHF) using the Normal vs CHF dataset, typically measured by Overall Accuracy. A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | Congestive Heart Failure Identification | | Dataset (including any stated split) | Normal vs Congestive Heart Failure (CHF) | | Metric | Overall Accuracy | | Unit | PERCENT | | Direction | Higher is better | | Results loaded | 10 of 10 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [Overall Accuracy](): Higher is better; unit: PERCENT. ## Recorded results | Method | Overall Accuracy | Result date | Report paper | | --- | --- | --- | --- | | Liu et al., 2014 | 100 | Not recorded | 68fa3a011e209131ce8c726a | | Majahan et al., 2017 | 99.5 | Not recorded | 68fa3a011e209131ce8c726a | | Orhan, 2013 | 99.3 | Not recorded | 68fa3a011e209131ce8c726a | | R-HessELM (IEM) | 98.41 | Not recorded | 68fa3a011e209131ce8c726a | | Acharya et al., 2017 | 97.64 | Not recorded | 68fa3a011e209131ce8c726a | | Yu and Lee, 2012 | 97.59 | Not recorded | 68fa3a011e209131ce8c726a | | Yu and Lee, 2012 | 96.38 | Not recorded | 68fa3a011e209131ce8c726a | | İşler and Kuntalp, 2007 | 93.98 | Not recorded | 68fa3a011e209131ce8c726a | | Narin et al., 2014 | 91.56 | Not recorded | 68fa3a011e209131ce8c726a | | Asyalı, 2003 | 89.95 | Not recorded | 68fa3a011e209131ce8c726a | ### Liu et al., 2014 Overall Accuracy: 100. Result ID: 698b97837a967eddb76fdb50. [Reporting paper]() Recorded evaluation configuration: {"Feature Model":"Three nonstandard HRV measures (i.e. SUM\_TD, SUM\_FD and SUM\_IE)","Classifier":"SVM"}. This record does not assert independent reproduction. ### Majahan et al., 2017 Overall Accuracy: 99.5. Result ID: 698b97837a967eddb76fdb52. [Reporting paper]() Recorded evaluation configuration: {"Feature Model":"Probabilistic symbol pattern Recognition","Classifier":"Decision Trees"}. This record does not assert independent reproduction. ### Orhan, 2013 Overall Accuracy: 99.3. Result ID: 698b97837a967eddb76fdb4f. [Reporting paper]() Recorded evaluation configuration: {"Feature Model":"Equal Frequency in Amplitude(EFiA), Equal Width in Time (EWIE)","Classifier":"Linear Regression"}. This record does not assert independent reproduction. ### R-HessELM (IEM) Overall Accuracy: 98.41. Result ID: 698b97837a967eddb76fdb54. [Reporting paper]() [Method origin paper]() [Implementation]() Recorded evaluation configuration: {"Feature Model":"Incline Entropy Measures (IEM) of SODP","Feature Size":"17","Classifier":"Regularized HessELM"}. This record does not assert independent reproduction. ### Acharya et al., 2017 Overall Accuracy: 97.64. Result ID: 698b97837a967eddb76fdb53. [Reporting paper]() Recorded evaluation configuration: {"Feature Model":"Empirical mode Decomposition","Feature Size":"13","Classifier":"SVM"}. This record does not assert independent reproduction. ### Yu and Lee, 2012 Overall Accuracy: 97.59. Result ID: 698b97837a967eddb76fdb4e. [Reporting paper]() Recorded evaluation configuration: {"Feature Model":"UCIMFS, MIFS, CMIFS, mRMR and MI-based greedy feature selection","Feature Size":"15","Classifier":"SVM"}. This record does not assert independent reproduction. ### Yu and Lee, 2012 Overall Accuracy: 96.38. Result ID: 698b97837a967eddb76fdb4d. [Reporting paper]() Recorded evaluation configuration: {"Feature Model":"Bispectrum-related features, Genetic feature selection","Classifier":"SVM"}. This record does not assert independent reproduction. ### İşler and Kuntalp, 2007 Overall Accuracy: 93.98. Result ID: 698b97837a967eddb76fdb4c. [Reporting paper]() Recorded evaluation configuration: {"Feature Model":"Poincare Plot, Fast Fourier Transform, Genetic feature selection","Feature Size":"9","Classifier":"k-nn"}. This record does not assert independent reproduction. ### Narin et al., 2014 Overall Accuracy: 91.56. Result ID: 698b97837a967eddb76fdb51. [Reporting paper]() Recorded evaluation configuration: {"Feature Model":"Wavelet Transform Backward elimination Method","Classifier":"SVM"}. This record does not assert independent reproduction. ### Asyalı, 2003 Overall Accuracy: 89.95. Result ID: 698b97837a967eddb76fdb4b. [Reporting paper]() Recorded evaluation configuration: {"Feature Model":"Standard Deviation, Normal to Normal, Wavelet Entropy, Poincare Plot","Classifier":"BayesNET"}. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.