Abstract
Heart Disease (HD) is a candidate for the utmost communal death-recording diseases in history and an early detection is a herculean task for countless physicians. This paper aims at developing a precise and efficient machine learning (ML) classification model for HD. The HD dataset was subjected to seven different machine learning models, including k-Nearest Neighbour (k-NN), eXtreme Gradient Boosting (XGBoost), Extra Trees (ET), Decision Tree (DT), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), and Random Forest (RF). Recall, precision, F1-Score, accuracy, ROC, and RPC were all used to evaluate the proposed models. The results obtained based on the aforementioned metrics in comparison to other models indicate that ET performed better. ET achieved 87% accuracy, precision (0.88), RPC (0.86), Recall (0.87), ROC (0.94), and F1-score (0.935) respectively. The outcomes indicates that ML models can classify HD patients effectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Odusami, M., Maskeliunas, R., Damaševičius, R., Misra, S.: Comparable study of pre-trained model on Alzheimer disease classification. In: Gervasi, O., et al. (eds.) ICCSA 2021. LNCS, vol. 12953, pp. 63–74. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86976-2_5
Durairaj, M., Ramasamy, N.: A comparison of the perceptive approaches for preprocessing the data set for predicting fertility success rate. Int. J. Control Theory Appl. 9(27), 255–260 (2016)
Udenwagu, N., Azeta, A., Misra, S., Nwaocha, V., Enosegbe, D., Sharma, M.: ExplainEx: an explainable artificial intelligence framework for interpreting predictive models. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, T.-P. (eds.) HIS 2020. AISC, vol. 1375, pp. 505–515. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73050-5_51
Awotunde, J., Folorunso, S., Bhoi, A., Adebayo, P., Ijaz, M.: Disease diagnosis system for IoT-based wearable body sensors with machine learning algorithm. In: Kumar Bhoi, A., Mallick, P.K., Narayana Mohanty, M., Ade Albuquerque, V.H.C. (eds.) Hybrid Artificial Intelligence and IoT in Healthcare. ISRL, vol. 209, pp. 201–222. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-2972-3_10
Ghwanmeh, S., Mohammad, A., Al-Ibrahim, A.: Innovative artificial neural networks-based decision support system for heart diseases diagnosis (2013)
Baumgartner, H., et al.: 2020 ESC Guidelines for the management of adult congenital heart disease: the Task Force for the management of adult congenital heart disease of the European Society of Cardiology (ESC). Eur. Heart J. 42(6), 563–645 (2021)
Karay, K.M., et al.: Clinical profiles and outcomes of heart failure in five African Countries: results from INTER-CHF study. Global Heart 16(1), 50 (2021)
López-Sendón, J.: The heart failure epidemic. Medicographia 33(4), 363–369 (2011)
Ndagire, E., et al.: Examining the Ugandan health system’s readiness to deliver rheumatic heart disease-related services. PLoS Negl. Trop. Dis. 15(2), e0009164 (2021)
Almustafa, K.M.: Prediction of heart disease and classifiers’ sensitivity analysis. BMC Bioinform. 21(278), 1–18 (2020)
Tougui, I., Jilbab, A., El Mhamdi, J.: Heart disease classification using data mining tools and machine learning techniques. Health Technol. 10, 137–1144 (2020)
Azeez, N., et al.: A fuzzy expert system for diagnosing and analyzing human diseases. In: Abraham, A., Gandhi, N., Pant, M. (eds.) IBICA 2018. AISC, vol. 939, pp. 474–484. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16681-6_47
Abdeldjouad, F.Z., Brahami, M., Matta, N.: A hybrid approach for heart disease diagnosis and prediction using machine learning techniques. In: Jmaiel, M., Mokhtari, M., Abdulrazak, B., Aloulou, H., Kallel, S. (eds.) ICOST 2020. LNCS, vol. 12157, pp. 299–306. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51517-1_26
El-Bialy, R., Salamay, M.A., Karam, H.O., Khalifa, M.E.: Feature analysis of coronary artery heart disease data sets. In: International Conference on Communication, Management and Information Technology (ICCMIT 2015) (2015)
Gao, X.-Y., Ali, A.A., Hassan, H.S., Anwar, E.M.: Improving the accuracy for analyzing heart diseases prediction based on the ensemble method. Complexity 2021(6663455), 10 (2021)
Spencer, R., Thabtah, F., Abdelhamid, N., Thompson, M.: Exploring feature selection and classification methods for predicting heart disease. Digit. Health 6, 1–10 (2020)
Ali, F., et al.: A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inf. Fusion 63, 208–222 (2020)
Beunza, J.J., et al.: Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease). J. Biomed. Inform. 97, 103257 (2019)
El Hamdaoui, H., Boujraf, S., Chaoui, N.E.H., Maaroufi, M.: A clinical support system for prediction of heart disease using machine learning techniques. In: 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (2020)
Kavitha, M., Gnaneswar, G., Dinesh, R., Sai, Y.R., Suraj, R.S.: Heart disease prediction using hybrid machine learning model. In: 6th International Conference on Inventive Computation Technologies (ICICT) (2021)
Aggrawal, R., Pal, S.: Sequential feature selection and machine learning algorithm-based patient’s death events prediction and diagnosis in heart disease. SN Comput. Sci. 1(6), 1–16 (2020)
Wu, J.H., et al.: Risk assessment of hypertension in steel workers based on LVQ and Fisher-SVM deep excavation. IEEE Access 7, 23109–23119 (2019)
Breiman, L.: Random forests. BMach. Learn. 45(1), 5–32 (2001)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System (2016)
Folorunso, S.O., Afolabi, S.A., Owodeyi, A.B.: Dissecting genre of nigerian music with machine learning models. J. King Saud Univ. Comput. Inf. Sci., 1–24 (2021)
Iheme, P.C., Nicholas, A., Omoregbe, S.M., Adeloye, D., Adewumi, A.O.: Mobile-bayesian diagnostic system for childhood infectious diseases, pp. 109–118 (2017)
Thompson, T., Sowunmi, O., Misra, S., Fernandez-Sanz, L., Crawford, B., Soto, R.: An expert system for the diagnosis of sexually transmitted diseases–ESSTD. J. Intell. Fuzzy Syst. 33(4), 2007–2017 (2017)
Cohen, S.: The basics of machine learning: strategies and techniques. In: Artificial Intelligence and Deep Learning in Pathology, pp. 13–40 (2021)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
Folorunso, S.O., Awotunde, J.B., Adeboye, N.O., Matiluko, O.E.: Data classification model for COVID-19 pandemic. In: Hassanien, A.-E., Elghamrawy, S.M., Zelinka, I. (eds.) Advances in Data Science and Intelligent Data Communication Technologies for COVID-19. SSDC, vol. 378, pp. 93–118. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-77302-1_6
Fitkov-Norris, E., Folorunso, S.O.: Impact of sampling on neural network classification performance in the context of repeat movie viewing. In: liadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013. CCIS, vol. 383. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41013-0
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Folorunso, S.O., Awotunde, J.B., Adeniyi, E.A., Abiodun, K.M., Ayo, F.E. (2022). Heart Disease Classification Using Machine Learning Models. In: Misra, S., Oluranti, J., Damaševičius, R., Maskeliunas, R. (eds) Informatics and Intelligent Applications. ICIIA 2021. Communications in Computer and Information Science, vol 1547. Springer, Cham. https://doi.org/10.1007/978-3-030-95630-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-95630-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95629-5
Online ISBN: 978-3-030-95630-1
eBook Packages: Computer ScienceComputer Science (R0)