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Machine learning based congestive heart failure detection using feature importance ranking of multimodal features

  • Received: 29 September 2020 Accepted: 03 November 2020 Published: 19 November 2020
  • In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

    Citation: Lal Hussain, Wajid Aziz, Ishtiaq Rasool Khan, Monagi H. Alkinani, Jalal S. Alowibdi. Machine learning based congestive heart failure detection using feature importance ranking of multimodal features[J]. Mathematical Biosciences and Engineering, 2021, 18(1): 69-91. doi: 10.3934/mbe.2021004

    Related Papers:

  • In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.


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    [1] H. F. Jelinek, D. J. Cornforth, A. H. Khandoker, ECG Time Series Variability Analysis, CRC Press, 2017.
    [2] A. J. Seely, P. T. Macklem, Complex systems and the technology of variability analysis, Crit. Care., 8 (2004), R367.
    [3] Y. İşler, M. Kuntalp, Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure, Comput. Biol. Med., 37 (2007), 1502-1510.
    [4] I. Awan, W. Aziz, I. H. Shah, N. Habib, J. S. Alowibdi, S. Saeed, et al., Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis, PLoS One., 13 (2018), e0196823.
    [5] W. Aziz, M. Rafique, I. Ahmad, M. Arif, N. Habib, M. Nadeem, Classification of heart rate signals of healthy and pathological subjects using threshold based symbolic entropy, Acta Biol. Hung., 65 (2014), 252-264. doi: 10.1556/ABiol.65.2014.3.2
    [6] A. Hossen, B. Al-Ghunaimi, A wavelet-based soft decision technique for screening of patients with congestive heart failure, Biomed. Signal Process. Control., 2 (2007), 135-143. doi: 10.1016/j.bspc.2007.05.008
    [7] R. A. Thuraisingham, A classification system to detect congestive heart failure uing second-order difference plot of RR intervals, Cardiol. Res. Pract., 2009 (2009), 1-7.
    [8] S. N. Yu, M. Y. Lee, Conditional mutual information-based feature selection for congestive heart failure recognition using heart rate variability, Comput. Methods Programs Biomed., 108 (2012), 299-309. doi: 10.1016/j.cmpb.2011.12.015
    [9] L. Pecchia, P. Melillo, M. Sansone, M. Bracale, Discrimination power of short-term heart rate variability easures for CHF assessment, IEEE Trans. Inf. Technol. Biomed., 15 (2011), 40-46. doi: 10.1109/TITB.2010.2091647
    [10] G. Altan, Y. Kutlu, N. Allahverdi, A new approach to early diagnosis of congestive heart failure disease by using Hilbert-Huang transform, Comput. Methods Programs Biomed., 137 (2016), 23-34. doi: 10.1016/j.cmpb.2016.09.003
    [11] G. I. Choudhary, W. Aziz, I. R. Khan, S. Rahardja, P. Franti, Analysing the dynamics of interbeat interval time series using grouped horizontal visibility graph, IEEE Access, 7 (2019), 9926-9934. doi: 10.1109/ACCESS.2018.2890542
    [12] Y. Isler, A. Narin, M. Ozer, M. Perc, Multi-stage classification of congestive heart failure based on short-term heart rate variability, Chaos Solitons Fractals, 118 (2019), 145-151. doi: 10.1016/j.chaos.2018.11.020
    [13] A. Narin, Y. Isler, M. Ozer, M. Perc, Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability, Phys. A Stat. Mech. Its Appl., 509 (2018), 56-65. doi: 10.1016/j.physa.2018.06.022
    [14] T. Jagrič, M. Marhl, D. Štajer, Š. T. Kocjančič, T. Jagrič, M. Podbregar, et al., Irregularity test for very short electrocardiogram (ECG) signals as a method for predicting a successful defibrillation in patients with ventricular fibrillation, Transl. Res., 149 (2007), 145-151.
    [15] L. Wang, W. Xue, Y. Li, M. Luo, J. Huang, W. Cui, et al., Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis, Entropy, 19 (2017), 222.
    [16] L. Hussain, W. Aziz, S. Saeed, I. A. Awan, A. A. Abbasi, N. Maroof, Arrhythmia detection by extracting hybrid features based on refined Fuzzy entropy (FuzEn) approach and employing machine learning techniques, Waves Random Complex Media, 30 (2020), 656-686. doi: 10.1080/17455030.2018.1554926
    [17] S. Rathore, M. Hussain, A. Khan, Automated colon cancer detection using hybrid of novel geometric features and some traditional features, Comput. Biol. Med., 65 (2015), 279-296. doi: 10.1016/j.compbiomed.2015.03.004
    [18] H. Peng, F. Long, C. Ding, Feature selection based on mutual information criteria of maxdependency, max-relevance, and min-redundancy, IEEE Trans. Pattern Anal. Mach. Intell., 27 (2005), 1226-1238.
    [19] H. Karnan, N. Sivakumaran, R. Manivel, An efficient cardiac arrhythmia onset detection technique using a novel feature rank score algorithm, J. Med. Syst., 43 (2019), 167.
    [20] M. G. Leguia, Z. Levnajić, L. Todorovski, B. Ženko, Reconstructing dynamical networks via feature ranking, Chaos Interd. J. Nonlinear Sci., 29 (2019), 093107.
    [21] Z. Zhou, S. Li, G. Qin, M. Folkert, S. Jiang, J. Wang, Multi-objective-based radiomic feature selection for lesion malignancy classification, IEEE J. Biomed. Heal. Inform., 24 (2020), 194-204. doi: 10.1109/JBHI.2019.2902298
    [22] M. Mourad, S. Moubayed, A. Dezube, Y. Mourad, K. Park, A. Torreblanca-Zanca, et al., Machine learning and feature selection applied to SEER data to reliably assess thyroid cancer prognosis, Sci. Rep., 10 (2020), 5176.
    [23] A. P. Bradley, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Patt. Recogn., 30 (1997), 1145-1159. doi: 10.1016/S0031-3203(96)00142-2
    [24] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, et al., PhysioBank, PhysioToolkit, and PhysioNet, Circulation, 101 (2000).
    [25] J. T. Bigger, J. L. Fleiss, R. C. Steinman, L. M. Rolnitzky, W. J. Schneider, P. K. Stein, RR variability in healthy, middle-aged persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction, Circulation, 91 (1995), 1936-1943.
    [26] J. E. Mietus, The pNNx files: Re-examining a widely used heart rate variability measure, Heart, 88 (2002), 378-380. doi: 10.1136/heart.88.4.378
    [27] K. L. Dodds, C. B. Miller, S. D. Kyle, N. S. Marshall, C. J. Gordon, Heart rate variability in insomnia patients: A critical review of the literature, Sleep Med. Rev., 33 (2017), 88-100. doi: 10.1016/j.smrv.2016.06.004
    [28] M. R. Esco, H. N. Williford, A. A. Flatt, T. J. Freeborn, F. Y. Nakamura, Ultra-shortened timedomain HRV parameters at rest and following exercise in athletes: An alternative to frequency computation of sympathovagal balance, Eur. J. Appl. Physiol., 118 (2018), 175-184. doi: 10.1007/s00421-017-3759-x
    [29] S. A. Geronikolou, K. Albanopoulos, G. Chrousos, D. Cokkinos, Evaluating the homeostasis assessment model insulin resistance and the cardiac autonomic system in bariatric surgery patients: A meta-analysis, in: P. Vlamos (Ed.), Springer International Publishing, Cham, 2017,249-259.
    [30] C. A. Sima, J. A. Inskip, A. W. Sheel, S. F. van Eeden, W. D. Reid, P. G. Camp, The reliability of short-term measurement of heart rate variability during spontaneous breathing in people with chronic obstructive pulmonary disease, Rev. Port. Pneumol. (English Ed.), 23 (2017), 338-342.
    [31] L. Hussain, Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach, Cogn. Neurodyn., 12 (2018), 271-294. doi: 10.1007/s11571-018-9477-1
    [32] L. Hussain, I. A. Awan, W. Aziz, S. Saeed, A. Ali, F. Zeeshan, et al., Detecting congestive heart failure by extracting multimodal features and employing machine learning techniques, Biomed. Res. Int., 2020 (2020), 1-19.
    [33] L. Hussain, W. Aziz, J. S. Alowibdi, N. Habib, M. Rafique, S. Saeed, et al., Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states, J. Physiol. Anthropol., 36 (2017).
    [34] L. Hussain, W. Aziz, A. A. Alshdadi, M. S. A. Nadeem, I. R. Khan, Q. U. A. Chaudhry, Analyzing the dynamics of lung cancer imaging data using refined fuzzy entropy methods by extracting different features, IEEE Access, 7 (2019), 64704-64721. doi: 10.1109/ACCESS.2019.2917303
    [35] L. Hussain, W. Aziz, S. Saeed, S. A. Shah, M. S. A. Nadeem, A. Awan, et al., Complexity analysis of EEG motor movement with eye open and close subjects using multiscale permutation entropy (MPE) technique, Biomed. Res., 28 (2017), 104-7111.
    [36] L. Hussain, W. Aziz, S. Saeed, S. A. Shah, M. S. A. Nadeem, I. A. Awan, et al., Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm, Biomed. Eng. Biomed. Tech., 63 (2018), 481-490.
    [37] L. Hussain, S. Saeed, A. Idris, I. A. Awan, S. A. Shah, A. Majid, et al., Regression analysis for detecting epileptic seizure with different feature extracting strategies, Biomed. Eng. Biomed. Tech., 64 (2019), 619-642.
    [38] M. Pincus, Approximate entropy as a measure of system complexity, Proc. Natl. Acad. Sci., 88 (1991), 2297-2301. doi: 10.1073/pnas.88.6.2297
    [39] J. S. Richman, J. R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, Am. J. Physiol. Circ. Physiol., 278 (2000), H2039-H2049.
    [40] D. Wang, D. Miao, C. Xie, Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection, Expert Syst. Appl., 38 (2011), 14314-14320.
    [41] O. A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann, et al., Wavelet entropy: A new tool for analysis of short duration brain electrical signals, J. Neurosci. Methods, 105 (2001), 65-75.
    [42] Y. Wu, Y. Zhou, G. Saveriades, S. Agaian, J.P. Noonan, P. Natarajan, Local Shannon entropy measure with statistical tests for image randomness, Inf. Sci. (Ny)., 222 (2013), 323-342. doi: 10.1016/j.ins.2012.07.049
    [43] S. Ekici, S. Yildirim, M. Poyraz, Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition, Expert Syst. Appl., 34 (2008), 2937-2944. doi: 10.1016/j.eswa.2007.05.011
    [44] E. Avci, D. Hanbay, A. Varol, An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition, Expert Syst. Appl., 33 (2007), 582-589. doi: 10.1016/j.eswa.2006.06.001
    [45] I. Turkoglu, A. Arslan, E. Ilkay, An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks, Comput. Biol. Med., 33 (2003), 319-331. doi: 10.1016/S0010-4825(03)00002-7
    [46] H. Wang, T. M. Khoshgoftaar, K. Gao, A comparative study of filter-based feature ranking techniques, in: 2010 IEEE Int. Conf. Inf. Reuse Integr., IEEE, 2010, 43-48.
    [47] H. Shakir, Y. Deng, H. Rasheed, T. M. R. Khan, Radiomics based likelihood functions for cancer diagnosis, Sci. Rep., 9 (2019), 9501.
    [48] W. Wu, C. Parmar, P. Grossmann, J. Quackenbush, P. Lambin, J. Bussink, et al, Exploratory study to identify radiomics classifiers for lung cancer histology, Front. Oncol., 6 (2016), 187-194.
    [49] Y. Saeys, I. Inza, P. Larranaga, A review of feature selection techniques in bioinformatics, Bioinformatics, 23 (2007), 2507-2517. doi: 10.1093/bioinformatics/btm344
    [50] L. Zhu, L. Miao, D. Zhang, Iterative laplacian score for feature selection, in: Proc. 18th Int. Conf. Neural Inf. Process. Syst., 2012, 80-87.
    [51] A. K. Farahat, A. Ghodsi, M. S. Kamel, Efficient greedy feature selection for unsupervised learning, Knowl. Inf. Syst., 35 (2013), 285-310. doi: 10.1007/s10115-012-0538-1
    [52] P. Mitra, C. A. Murthy, S. K. Pal, Unsupervised feature selection using feature similarity, IEEE Trans. Pattern Anal. Mach. Intell., 24 (2002), 301-312. doi: 10.1109/34.990133
    [53] D. Cai, C. Zhang, X. He, Unsupervised feature selection for multi-cluster data, in: Proc. 16th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. - KDD '10, ACM Press, New York, New York, USA, New York, USA, 2010,333.
    [54] H. Zeng, Y. Cheung, Feature selection and Kernel learning for local learning-based clustering, IEEE Trans. Pattern Anal. Mach. Intell., 33 (2011), 1532-1547. doi: 10.1109/TPAMI.2010.215
    [55] Z. Zhao, H. Liu, Spectral feature selection for supervised and unsupervised learning, in: Proc. 24th Int. Conf. Mach. Learn. - ICML '07, ACM Press, New York, New York, USA, 2007, 1151-1157.
    [56] W. Yang, K. Wang, W. Zuo, Neighborhood component feature selection for high-dimensional data, J. Comput., 7 (2012), 161-168.
    [57] I. Kononenko, E. Šimec, M. Robnik-Šikonja, Overcoming the myopia of inductive learning algorithms with RELIEFF, Appl. Intell., 7 (1997), 39-55.
    [58] G. Rofo, S. Melzi, Ranking to learn: Feature ranking and selection via eigenvector centrality, in: A. Appice, M. Ceci, C. Loglisci, E. Masciari and Z. Ras, (eds.) New Frontiers in Mining Complex Patterns: 5th International Workshop, NFMCP 2016.
    [59] P. Bradley, O. Mangasarian, Feature selection via concave minimization and support vector machines, in: Proc. Int. Conf. Mach. Learn., 1998, : pp. 82-90.
    [60] S. Yu, Z. Zhang, X. Liang, J. Wu, E. Zhang, W. Qin, et al., A Matlab toolbox for feature importance ranking, in: 2019 Int. Conf. Med. Imaging Phys. Eng., IEEE, 2019, : pp. 1-6.
    [61] V. N. Vapnik, An overview of statistical learning theory, IEEE Trans. Neural Networks, 10 (1999), 988-999. doi: 10.1109/72.788640
    [62] P. Toccaceli, A. Gammerman, Combination of conformal predictors for classification, Proc. Sixth Work. Conform. Probabilistic Predict. Appl., 60 (2017), 39-61.
    [63] A. Subasi, Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders, Comput. Biol. Med., 43 (2013), 576-586. doi: 10.1016/j.compbiomed.2013.01.020
    [64] W. Liu, S. Chawla, D. A. Cieslak, N. V. Chawla, A robust decision tree algorithm for imbalanced data sets, in: Proc. 2010 SIAM Int. Conf. Data Min., Society for Industrial and Applied Mathematics, Philadelphia, PA, 2010,766-777.
    [65] M. J. Aitkenhead, A co-evolving decision tree classification method, Expert Syst. Appl., 34 (2008), 18-25. doi: 10.1016/j.eswa.2006.08.008
    [66] R.Wang, S. Kwong, X.Wang, Q. Jiang, Segment based decision tree induction with continuous valued attributes, IEEE Trans. Cybern., 45 (2015), 1262-1275. doi: 10.1109/TCYB.2014.2348012
    [67] J. J. Rissanen, Fisher information and stochastic complexity, IEEE Trans. Inf. Theory., 42 (1996), 40-47. doi: 10.1109/18.481776
    [68] A. Zaidi, B. Ould Bouamama, M. Tagina, Bayesian reliability models of Weibull systems: State of the art, Int. J. Appl. Math. Comput. Sci., 22 (2012), 585-600. doi: 10.2478/v10006-012-0045-2
    [69] P. Zhang, B.J. Gao, X. Zhu, L. Guo, Enabling fast lazy learning for data streams, in: 2011 IEEE 11th Int. Conf. Data Min., IEEE, 2011,932-941.
    [70] F. Schwenker, E. Trentin, Pattern classification and clustering: A review of partially supervised learning approaches, Pattern Recognit. Lett., 37 (2014), 4-14. doi: 10.1016/j.patrec.2013.10.017
    [71] K. Hajian-Tilaki, Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation, Casp. J. Intern. Med., 4 (2013), 627-635.
    [72] Y. Li, Y. Zhang, L. Zhao, Y. Zhang, C. Liu, L. Zhang, et al., Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure, IEEE Access, 6 (2018), 39734-39744.
    [73] A. Narin, Y. Isler, M. Ozer, Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance, Comput. Biol. Med., 45 (2014), 72-79. doi: 10.1016/j.compbiomed.2013.11.016
    [74] Işler, M. Kuntalp, Heart rate normalization in the analysis of heart rate variability in congestive heart failure, Proc. Inst. Mech. Eng. Part H J. Eng. Med., 224 (2010), 453-463. doi: 10.1243/09544119JEIM642
    [75] N. Elfadil, I. Ibrahim, Self organizing neural network approach for identification of patients with Congestive Heart Failure, in: 2011 Int. Conf. Multimed. Comput. Syst., IEEE, 2011, 1-6.
    [76] G. Yang, Y. Ren, Q. Pan, G. Ning, S. Gong, G. Cai, et al., A heart failure diagnosis model based on support vector machine, in: 2010 3rd Int. Conf. Biomed. Eng. Informatics, IEEE, 2010, 1105-1108.
    [77] C. S. Son, Y. N. Kim, H. S. Kim, H. S. Park, M. S. Kim, Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches, J. Biomed. Inform., 45 (2012), 999-1008 doi: 10.1016/j.jbi.2012.04.013
    [78] L. Hussain, W. Aziz, S. Saeed, A. Idris, I.A. Awan, S.A. Shah, et al., Spatial wavelet-based coherence and coupling in EEG signals with eye open and closed during resting state, IEEE Access, 6 (2018), 37003-37022.
    [79] L. Hussain, S. Rathore, A. A. Abbasi, S. Saeed, Automated lung cancer detection based on multimodal features extracting strategy using machine learning techniques, in: H. Bosmans, G. H. Chen, T. Gilat Schmidt (Eds.), Med. Imaging 2019 Phys. Med. Imaging, SPIE, 2019,134.
    [80] V. Singh, G. Kumari, B. Chhajer, A. K. Jhingan, S. Dahiya, Effectiveness of enhanced external counter pulsation on clinical profile and health-related quality of life in patients with coronary heart disease: A systematic review, Acta Angiol., 24 (2018), 105-122. doi: 10.5603/AA.2018.0021
    [81] Y. Isler, A. Narin, M. Ozer, Comparison of the effects of cross-validation methods on determining performances of classifiers used in diagnosing congestive heart failure, Meas. Sci. Rev., 15 (2015), 196-201. doi: 10.1515/msr-2015-0027
    [82] R. Han, X. Liu, M. Zheng, R. Zhao, X. Liu, X. Yin, et al., Effect of remote ischemic preconditioning on left atrial remodeling and prothrombotic response after radiofrequency catheter ablation for atrial fibrillation, Pacing Clin. Electrophysiol., 41 (2018), 246-254
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