PERFORMANCE EVALUATION OF MULTILAYER PERCEPTRON ARTIFICIAL NEURAL NETWORK MODEL IN THE CLASSIFICATION OF HEART FAILURE

Abstract

Abstract Objective: The aim of this study was to compare the classification performance of heart failure using the MLP ANN model on an open-access “heart failure clinical records” data set, as well as to identify risk factors that may be linked to heart failure. Material and Methods: The open-access “heart failure” data collection MLP ANN model was used to classify nephritis of the renal pelvis, and risk factors that may be involved were discovered. Different output metrics are used to demonstrate MLP ANN's progress. Results: It has been shown that the classification of renal pelvic nephritis is quite high with MLP ANN model (AUC = 0.925, Accuracy = 93.9%, Balanced Accuracy = 89.2%, Sensitivity = 98.4%, Specificity = 80.0%). Furthermore, the MLP ANN model showed that “time” is the most significant variable among the risk factors linked to heart failure. Conclusion: Consequently, in the analysis with the heart failure data collection, the MLP ANN model generated very positive results. Moreover, this model has gained important information in identifying risk factors that may be associated with heart failure. Thus, it has been understood that the relevant model will provide reliable information about any disease to be used in preventive medicine practices.

Keywords

• Classification
• Multilayer perceptron
• Artificial neural network
• Heart failure

References

1. L. R. Loehr, W. D. Rosamond, P. P. Chang, A. R. Folsom, and L. E. Chambless, “Heart Failure Incidence and Survival (from the Atherosclerosis Risk in Communities Study),” Am. J. Cardiol., vol. 101, no. 7, pp. 1016–1022, Apr. 2008, doi: 10.1016/j.amjcard.2007.11.061.
2. K. K. L. Ho, J. L. Pinsky, W. B. Kannel, and D. Levy, “The epidemiology of heart failure: The Framingham Study,” J. Am. Coll. Cardiol., vol. 22, no. 4 SUPPL. 1, pp. A6–A13, Oct. 1993, doi: 10.1016/0735-1097(93)90455-A.
3. A. Maestre, V. Gil, J. Gallego, J. Aznar, A. Mora, and A. Martín-Hidalgo, “Diagnostic accuracy of clinical criteria for identifying systolic and diastolic heart failure: cross-sectional study,” J. Eval. Clin. Pract., vol. 15, no. 1, pp. 55–61, Feb. 2009, doi: 10.1111/j.1365-2753.2008.00954.x.
4. A. Jimeno Sainz, V. Gil, J. Merino, M. García, A. Jordán, and L. Guerrero, “Validity of the Framingham criteria as a clinical test for systolic heart failure,” Rev. Clin. Esp., vol. 206, no. 10, pp. 495–498, Nov. 2006, doi: 10.1016/s0014-2565(06)72875-2.
5. E. Öztemel, Yapay Sinir Ağları, 2nd ed. Papatya Yayıncılık, 2006.
6. S. S. Haykin, Neural Networks: A comprehensive Foundation. New Jersey: Prentice Hall, 1999.
7. A. Arı, M. Erşen Berberler, D. Eylül Üniversitesi, B. Bilimleri Bölümü Murat Erşen Berberler, and B. Bilimleri Bölümü, “ACTA INFOLOGICA-2017-1 (2) Yapay Sinir Ağları ile Tahmin ve Sınıflandırma Problemlerinin Çözümü İçin Arayüz Tasarımı Information of Author(s),” Acta Infologica, vol. 1, no. 2, pp. 55–73, Dec. 2017, Accessed: Apr. 10, 2021. [Online]. Available: https://dergipark.org.tr/tr/pub/acin/335553.
8. D. Chicco and G. Jurman, “Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone,” BMC Med. Inform. Decis. Mak., vol. 20, no. 1, p. 16, Feb. 2020, doi: 10.1186/s12911-020-1023-5.

9. U. Orhan, M. Hekim, and M. Özer, “EEG i̇şaretlerinin çok-katmanli algilayici yapay sinir aǧi modeli ile siniflandirilmasinda ayriklaştirma yaklaşimi,” 2010, doi: 10.1109/BIYOMUT.2010.5479842.
10. G. BLEUMINK et al., “Quantifying the heart failure epidemic: Prevalence, incidence rate, lifetime risk and prognosis of heart failure - The Rotterdam Study,” Eur. Heart J., vol. 25, no. 18, pp. 1614–1619, Sep. 2004, doi: 10.1016/j.ehj.2004.06.038.
11. A. Mosterd and A. W. Hoes, “Clinical epidemiology of heart failure,” Heart, vol. 93, no. 9. BMJ Publishing Group Ltd, pp. 1137–1146, Sep. 01, 2007, doi: 10.1136/hrt.2003.025270.
12. A. A. Heidari, H. Faris, I. Aljarah, and S. Mirjalili, “An efficient hybrid multilayer perceptron neural network with grasshopper optimization,” Soft Comput., vol. 23, no. 17, pp. 7941–7958, Sep. 2019, doi: 10.1007/s00500-018-3424-2.
13. H. Faris, I. Aljarah, and S. Mirjalili, “Training feedforward neural networks using multi-verse optimizer for binary classification problems,” Appl. Intell., vol. 45, no. 2, pp. 322–332, Sep. 2016, doi: 10.1007/s10489-016-0767-1.
14. Y. C. Hu, “Nonadditive similarity-based single-layer perceptron for multi-criteria collaborative filtering,” Neurocomputing, vol. 129, pp. 306–314, Apr. 2014, doi: 10.1016/j.neucom.2013.09.027.
15. V. K. Ojha, A. Abraham, and V. Snášel, “Metaheuristic design of feedforward neural networks: A review of two decades of research,” Eng. Appl. Artif. Intell., vol. 60, pp. 97–116, Apr. 2017, doi: 10.1016/j.engappai.2017.01.013.
16. J.-F. Chen, Q. Do, and H.-N. Hsieh, “Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm,” Algorithms, vol. 8, no. 2, pp. 292–308, Jun. 2015, doi: 10.3390/a8020292.
17. Z. Kucukakcali, I. B. Cicek, E. Guldogan, and C. Colak, “ASSESSMENT OF ASSOCIATIVE CLASSIFICATION APPROACH FOR PREDICTING MORTALITY BY HEART FAILURE,” J. Cogn. Syst., vol. 5, no. 2, pp. 41–45, Dec. 2020, Accessed: Apr. 10, 2021. [Online]. Available: http://dergipark.gov.tr/jcs.