Abstract
The accurate prediction of heart disease is crucial in the field of medicine. While convolutional neural networks have shown remarkable precision in heart disease prediction, they are often perceived as opaque models due to their complex internal workings. This paper introduces a novel method, named Extraction of Classification Rules from Convolutional Neural Network (ECRCNN), aimed at extracting rules from convolutional neural networks to enhance interpretability in heart disease prediction. The ECRCNN algorithm analyses updated kernels to derive understandable rules from convolutional neural networks, providing valuable insights into the contributing factors of heart disease. The algorithm’s performance is assessed using the Statlog (Heart) dataset from the University of California, Irvine’s repository. Experimental results underscore the effectiveness of the ECRCNN algorithm in predicting heart disease and extracting meaningful rules. The extracted rules can assist healthcare professionals in making precise diagnoses and formulating targeted treatment plans. In summary, the proposed method bridges the gap between the high accuracy of convolutional neural networks and the interpretability necessary for informed decision-making in heart disease prediction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Fuchs FD, Whelton PK. High blood pressure and cardiovascular disease. Hypertension. 2020;75(2):285–92.
Jankowski J, Floege J, Fliser D, Böhm M, Marx N. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation. 2021;143(11):1157–72.
Cardiovascular diseases (CVDs), 2021. Available from: https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Chakraborty M, Biswas SK. Computer-aided heart disease diagnosis using recursive rule extraction algorithms from neural networks. Int J Comput Intell Appl. 2022;21(02):2250011.
Cardiovascular disease is on the rise, but we know how to curb it. We’ve done it before. Available from: https://www.nhlbi.nih.gov/news/2021/cardiovascular-disease-rise-we-know-how-curb-it-weve-done-it
Mehra MR, Desai SS, Kuy S, Henry TD, Patel AN. Cardiovascular disease, drug therapy, and mortality in Covid-19. N Engl J Med. 2020;382(25): e102.
Asri H, Mousannif H, Al Moatassime H, Noel T. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci. 2016;83:1064–9.
Deepika M, Kalaiselvi K. A empirical study on disease diagnosis using data mining techniques. In: 2018 second international conference on inventive communication and computational technologies (ICICCT), IEEE; 2018. pp. 615–620.
Ramalingam VV, Dandapath A, Raja MK. Heart disease prediction using machine learning techniques: a survey. Int J Eng Technol. 2018;7(2.8):684–7.
Rajdhan A, Agarwal A, Sai M, Ravi D, Ghuli P. Heart disease prediction using machine learning. Int J Eng Res Technol. 2020;9(4):659–62.
Dwivedi AK. Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput Appl. 2018;29:685–93.
Mohan S, Thirumalai C, Srivastava G. Effective heart disease prediction using hybrid machine learning techniques. IEEE Access. 2019;7:81542–54.
Chakraborty M, Biswas SK, Purkayastha B. Data mining using neural networks in the form of classification rules: a review. In: 2020 4th international conference on computational intelligence and networks (CINE), IEEE; 2020. pp. 1–6.
Setiono R, Liu H. Symbolic representation of neural networks. Computer. 1996;29(3):71–7.
Towell GG, Shavlik JW. Extracting refined rules from knowledge-based neural networks. Mach Learn. 1993;13:71–101.
Odajimaa K, Hayashi Y, Tianxia G, Setiono R. Greedy rule generation from discrete data and its use in neural network rule extraction. Neural Netw. 2008;21(7):1020–8.
Setiono R, Baesens B, Mues C. Recursive neural network rule extraction for data with mixed attributes. IEEE Trans Neural Netw. 2008;19(2):299–307.
Augasta MG, Kathirvalavakumar T. Reverse engineering the neural networks for rule extraction in classification problems. Neural Process Lett. 2012;35:131–50.
Zilke JR, Loza ME, Janssen F. DeepRED—rule extraction from deep neural networks. In: Calders T, Ceci M, Malerba D, editors. Discovery science. DS 2016. Lecture notes in computer science. Vol. 9956. Bari, Italy: Springer, Cham; 2016.
Chakraborty M, Biswas SK, Purkayastha B. Recursive rule extraction from NN using reverse engineering technique. N Gener Comput. 2018;36:119–42.
Chakraborty M, Biswas SK, Purkayastha B. Rule extraction from neural network using input data ranges recursively. N Gener Comput. 2019;37:67–96.
Chakraborty M, Biswas SK, Purkayastha B. Rule extraction from neural network trained using deep belief network and back propagation. Knowl Inf Syst. 2020;62:3753–81.
Chakraborty M, Biswas SK, Purkayastha B. Rule extraction using ensemble of neural network ensembles. Cogn Syst Res. 2022;75:36–52.
Bakar WA, Josdi NL, Man MB, Zuhairi MA. A review: heart disease prediction in machine learning and deep learning. In: 2023 19th IEEE international colloquium on signal processing and its applications (CSPA), IEEE; 2023. pp. 150–155.
Funding
No funds, grants, or other support was received.
Author information
Authors and Affiliations
Contributions
MC—contributed to the study and design of the algorithm, experimentation and result analysis, and manuscript writing.
Corresponding author
Ethics declarations
Conflict of interest
The author have no relevant financial or non-financial interests to disclose.
Ethical approval
No human or animal is involved.
Consent to participate
Not applicable.
Consent to publish
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chakraborty, M. Rule extraction from convolutional neural networks for heart disease prediction. Biomed. Eng. Lett. (2024). https://doi.org/10.1007/s13534-024-00358-3
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13534-024-00358-3