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Integrating Machine Learning in Clinical Decision Support for Heart Failure Diagnosis: Case Study

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MEDICON’23 and CMBEBIH’23 (MEDICON 2023, CMBEBIH 2023)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 93))

Abstract

Heart failure is the leading cause of hospitalization in people older than 65. Accurate referrals can reduce the devastating impact of heart failure. Timely diagnosis of heart failure from other cardiovascular conditions based only on symptoms is a major challenge. Machine learning has demonstrated potential for overcoming the diagnostic challenges of cardiovascular diseases. Many research papers are now focusing on application of artificial intelligence methods applied to diagnosis of heart failure, where databases continue to be a limitation. The current study used a dataset of 368 patients (297 patients with diagnosed heart failure, 71 control subjects) from an upper middle-income country, containing information on subject population characteristics, symptoms and laboratory test results. Manual feature selection was performed, focusing on clinical symptoms that are easily measurable. Four common machine learning methods were tested and compared: Decision Tree (DT) algorithm, Random Forest (RF) algorithm, Support Vector Machine (SVM) and Naïve Bayes (NB) algorithm. Models were developed through a holdout process of training-validation and testing. Our final model was a Decision Tree, achieving an AUC of 94.3%, with the advantage of being fully intelligible and easily interpreted. The performance achieved suggested that intelligible machine learning models can enhance symptom-based referral of heart failure.

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References

  1. Di Palo, K.E., Barone, N.J.: Hypertension and heart failure: Prevention, targets, and treatment. Cardiol. Clin. 40(2), 237–244 (2022). https://doi.org/10.1016/j.ccl.2021.12.011

    Article  Google Scholar 

  2. Arrigo, M., et al.: Acute heart failure. Nat. Rev. Dis. Primers. 6(1), 16 (2020). https://doi.org/10.1038/s41572-020-0151-7

    Article  MathSciNet  Google Scholar 

  3. Šećkanović, A., Šehovac, M., Spahić, L., Ramić, I., Mamatnazarova, N., Gurbeta Pokvić, L., Badnjević, A., Kacila, M.: Review of artificial intelligence application in cardiology. In: 2020 9th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, pp. 1–5 (2020) doi:https://doi.org/10.1109/MECO49872.2020.9134333

  4. WHO: Retrieved from: https://apps.who.int/iris/bitstream/handle/10665/252410/9789241511636-eng.pdf, January 10th (2020)

  5. Visalakshi, S., Radha, V.: A literature review of feature selection techniques and applications: Review of feature selection in Data Mining. In: 2014 IEEE International Conference on Computational Intelligence and Computing Research (2014) doi:https://doi.org/10.1109/iccic.2014.7238499

  6. Sarić, R., Jokić, D., Beganović, N., Pokvić, L.G., Badnjević, A.: FPGA-based real-time epileptic seizure classification using Artificial Neural Network. Biomed. Signal Process. Control 62, 102106 (2020). https://doi.org/10.1016/j.bspc.2020.102106

    Article  Google Scholar 

  7. Stokes, K., et al.: A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings. Biocybern. Biomed. Eng. 41(4), 1288–1302 (2021). https://doi.org/10.1016/j.bbe.2021.09.002

    Article  Google Scholar 

  8. Johnson, K.W., et al.: Artificial intelligence in cardiology. J. Am. Coll. Cardiol. 71(23), 2668–2679 (2018). https://doi.org/10.1016/j.jacc.2018.03.521

    Article  Google Scholar 

  9. Begic, E., et al.: From heart murmur to echocardiography—congenital heart defects diagnostics using machine-learning algorithms. Psychiatr. Danub. 33(Suppl 13), 236–246 (2021)

    Google Scholar 

  10. Ponikowski, P., Voors, A. A., Anker, S. D., Bueno, H., Cleland, J. G. F., Coats, A. J. S., Falk, V., González-Juanatey, J. R., Harjola, V. P., Jankowska, E. A., Jessup, M., Linde, C., Nihoyannopoulos, P., Parissis, J. T., Pieske, B., Riley, J. P., Rosano, G. M. C., Ruilope, L. M., Ruschitzka, F., Rutten, F. H., ESC Scientific Document Group: 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur. Hear. J. 37(27), 2129–2200 (2016)https://doi.org/10.1093/eurheartj/ehw128

  11. Choi, D.J., Park, J.J., Ali, T., Lee, S.: Artificial intelligence for the diagnosis of heart failure. NPJ Digit. Med. 3, 54 (2020). https://doi.org/10.1038/s41746-020-0261-3

    Article  Google Scholar 

  12. Cao, Z., Jia, Y., Zhu, B.: BNP and NT-proBNP as diagnostic biomarkers for cardiac dysfunction in both clinical and forensic medicine. Int. J. Mol. Sci. 20(8), 1820 (2019). https://doi.org/10.3390/ijms20081820

    Article  Google Scholar 

  13. Alcidi, G., Goffredo, G., Correale, M., Brunetti, N.D., Iacoviello, M.: Brain natriuretic peptide biomarkers in current clinical and therapeutic scenarios of heart failure. J. Clin. Med. 11(11), 3192 (2022). https://doi.org/10.3390/jcm11113192

    Article  Google Scholar 

  14. Lim, T.K., Ashrafian, H., Dwivedi, G., Collinson, P.O., Senior, R.: Increased left atrial volume index is an independent predictor of raised serum natriuretic peptide in patients with suspected heart failure but normal left ventricular ejection fraction: Implication for diagnosis of diastolic heart failure. Eur. J. Heart Fail. 8(1), 38–45 (2006). https://doi.org/10.1016/j.ejheart.2005.05.008

    Article  Google Scholar 

  15. Kim, M.K., et al.: Tissue Doppler-derived E/e’ ratio as a parameter for assessing diastolic heart failure and as a predictor of mortality in patients with chronic kidney disease. Korean J. Intern. Med. 28(1), 35–44 (2013). https://doi.org/10.3904/kjim.2013.28.1.35

    Article  Google Scholar 

  16. Strachinaru, M., et al.: Relation between E/e’ ratio and NT-proBNP levels in elderly patients with symptomatic severe aortic stenosis. Cardiovasc. Ultrasound 13, 29 (2015). https://doi.org/10.1186/s12947-015-0021-8

    Article  Google Scholar 

  17. Park, S.J., et al.: N-terminal pro-B-type natriuretic Peptide in overweight and obese patients with and without diabetes: An analysis based on body mass index and left ventricular geometry. Korean Circ. J. 39(12), 538–544 (2009). https://doi.org/10.4070/kcj.2009.39.12.538

    Article  Google Scholar 

  18. Gupta, D.K., Wang, T.J.: Natriuretic peptides and cardiometabolic health. Circ. J. 79(8), 1647–1655 (2015). https://doi.org/10.1253/circj.CJ-15-0589

    Article  Google Scholar 

  19. Sobhani, K., et al.: Sex differences in ischemic heart disease and heart failure biomarkers. Biol. Sex Differ. 9(1), 43 (2018). https://doi.org/10.1186/s13293-018-0201-y

    Article  Google Scholar 

  20. Omar, H.R., Guglin, M.: Acute systolic heart failure with normal admission BNP: Clinical features and outcomes. Int. J. Cardiol. 232, 324–329 (2017). https://doi.org/10.1016/j.ijcard.2016.12.069

    Article  Google Scholar 

  21. Mutlak, D., Lessick, J., Khalil, S., Yalonetsky, S., Agmon, Y., Aronson, D.: Tricuspid regurgitation in acute heart failure: Is there any incremental risk? Eur. Heart J. Cardiovasc. Imaging 19(9), 993–1001 (2018). https://doi.org/10.1093/ehjci/jex343

    Article  Google Scholar 

  22. Qiu, X., Liu, C., Ye, Y., Li, H., Chen, Y., Fu, Y., Liu, Z., Huang, X., Zhang, Y., Liao, X., Liu, H., Zhao, W., Liu, X.: The diagnostic value of serum creatinine and cystatin c in evaluating glomerular filtration rate in patients with chronic kidney disease: A systematic literature review and meta-analysis. Oncotarget 8(42), 72985–72999 (2017) https://doi.org/10.18632/oncotarget.20271

  23. Einwoegerer, C.F., Domingueti, C.P.: Association between increased levels of cystatin c and the development of cardiovascular events or mortality: A systematic review and meta-analysis. Arq. Bras. Cardiol. 111(6), 796–807 (2018). https://doi.org/10.5935/abc.20180171

    Article  Google Scholar 

  24. Falcão, F. J. A., Oliveira, F. R. A., Cantarelli, F., Cantarelli, R., Brito-Júnior, P., Lemos, H., Silva, P., Camboim, I., Freire, M. C., Carvalho, O., & Sobral-Filho, D. C.: Carbohydrate antigen 125 predicts pulmonary congestion in patients with ST-segment elevation myocardial infarction. Braz. J. Med. Biol. Res. = Rev. Bras. Pesqui. Medicas E Biol. 52(12), e9124 (2019) doi:https://doi.org/10.1590/1414-431X20199124

  25. Durak-Nalbantic, A., et al.: Predictors of hospitalization for heart failure decompensation in 18-months follow-up after index hospitalization for acute heart failure. Med. Arch. (Sarajev., Bosnia Herzeg.) 72(4), 257–261 (2018). https://doi.org/10.5455/medarh.2018.72.257-261

    Article  Google Scholar 

  26. Durak-Nalbantić, A., Džubur, A., Dilić, M., Pozderac, Z., Mujanović-Narančić, A., Kulić, M., Hodžić, E., Resić, N., Brdjanović, S., Zvizdić, F.: Brain natriuretic peptide release in acute myocardial infarction. Bosn. J. Basic Med. Sci. 12(3), 164–168 (2012) doi:https://doi.org/10.17305/bjbms.2012.2470

  27. Durak-Nalbantic, A., et al.: Serum level of tumor marker carbohydrate antigen-CA125 in heart failure. Med. Arch. (Sarajev., Bosnia Herzeg.) 67(4), 241–244 (2013). https://doi.org/10.5455/medarh.2013.67.241-244

    Article  Google Scholar 

  28. Donal, E., et al.: New echocardiographic predictors of clinical outcome in patients presenting with heart failure and a preserved left ventricular ejection fraction: A subanalysis of the Ka (Karolinska) Ren (Rennes) Study. Eur. J. Heart Fail. 17(7), 680–688 (2015). https://doi.org/10.1002/ejhf.291

    Article  Google Scholar 

  29. Ponikowski, P., et al.: Heart failure: preventing disease and death worldwide. ESC Hear. Fail. 1(1), 4–25 (2014). https://doi.org/10.1002/ehf2.12005

    Article  Google Scholar 

  30. Aljaaf, A. J., Al-Jumeily, D., Hussain, A. J., Dawson, T., Fergus, P., Al-Jumaily, M.: Predicting the likelihood of heart failure with a multi-level risk assessment using decision tree. In: 2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE) (2015) doi:https://doi.org/10.1109/taeece.2015.7113608

  31. Guo, A., Pasque, M., Loh, F., Mann, D.L., Payne, P.R.: Heart failure diagnosis, readmission, and mortality prediction using machine learning and Artificial Intelligence Models. Curr. Epidemiol. Rep. 7(4), 212–219 (2020). https://doi.org/10.1007/s40471-020-00259-w

    Article  Google Scholar 

  32. Shameer, K., et al.: Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: A case-study using mount Sinai heart failure cohort. Pac. Symp. Biocomput. 22, 276–287 (2017). https://doi.org/10.1142/9789813207813_0027

    Article  Google Scholar 

  33. Pană, M.-A., et al.: Reducing the heart failure burden in Romania by predicting congestive heart failure using Artificial Intelligence: Proof of concept. Appl. Sci. 11(24), 11728 (2021). https://doi.org/10.3390/app112411728

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Verlab Research Institute for Biomedical Engineering, Medical Devices and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina

    Lemana Spahić, Adna Softić, Azra Durak-Nalbantić, Edin Begić, Bojan Stanetić & Haris Vranić

  2. International Burch University Sarajevo, Sarajevo, Bosnia and Herzegovina

    Lemana Spahić, Adna Softić, Azra Durak-Nalbantić, Edin Begić, Bojan Stanetić & Haris Vranić

  3. Bioengineering Research and Development Center BioIRC, Kragujevac, Serbia

    Lemana Spahić, Adna Softić, Azra Durak-Nalbantić, Edin Begić, Bojan Stanetić & Haris Vranić

  4. Clinic for Heart, Blood Vessels and Rheumatism, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Lemana Spahić, Adna Softić, Azra Durak-Nalbantić, Edin Begić, Bojan Stanetić & Haris Vranić

  5. Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina

    Lemana Spahić, Adna Softić, Azra Durak-Nalbantić, Edin Begić, Bojan Stanetić & Haris Vranić

  6. General Hospital Abdulah Nakaš, Sarajevo, Bosnia and Herzegovina

    Lemana Spahić, Adna Softić, Azra Durak-Nalbantić, Edin Begić, Bojan Stanetić & Haris Vranić

  7. University Clinical Centre of the Republic of Srpska, Banja Luka, Bosnia and Herzegovina

    Lemana Spahić, Adna Softić, Azra Durak-Nalbantić, Edin Begić, Bojan Stanetić & Haris Vranić

  8. Medical Faculty, University of Banja Luka, Banja Luka, Bosnia and Herzegovina

    Lemana Spahić, Adna Softić, Azra Durak-Nalbantić, Edin Begić, Bojan Stanetić & Haris Vranić

  9. Sarajevo School of Science and Technology, 71 000, Sarajevo, Bosnia and Herzegovina

    Lemana Spahić, Adna Softić, Azra Durak-Nalbantić, Edin Begić, Bojan Stanetić & Haris Vranić

Authors
  1. Lemana Spahić
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  2. Adna Softić
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  3. Azra Durak-Nalbantić
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  4. Edin Begić
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Corresponding author

Correspondence to Adna Softić .

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Editors and Affiliations

  1. Verlab Research Institute for Biomedical Engineering, Medical Devices, and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina

    Almir Badnjević

  2. Verlab Research Institute for Biomedical Engineering, Medical Devices, and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina

    Lejla Gurbeta Pokvić

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Spahić, L., Softić, A., Durak-Nalbantić, A., Begić, E., Stanetić, B., Vranić, H. (2024). Integrating Machine Learning in Clinical Decision Support for Heart Failure Diagnosis: Case Study. In: Badnjević, A., Gurbeta Pokvić, L. (eds) MEDICON’23 and CMBEBIH’23. MEDICON CMBEBIH 2023 2023. IFMBE Proceedings, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-031-49062-0_73

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  • DOI: https://doi.org/10.1007/978-3-031-49062-0_73

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