Abstract
Coronary heart disease (CHD) is a significant medical disorder and one of the most prevalent forms of heart disease. Owing to the reality that a heart attack will happen without notice, an insightful screening system is inevitable. This paper investigates a new CHD detection approach built on an optimization machine learning technique, such as classifier ensembles. To boost the efficiency of our system, we used the Feature-Selector optimization model to select the best subset of CHD features. Second, to solve the problem of imbalanced CHD data, we used optimized SMOTE over-sampling, a highly efficient approach embedded with an optimization model. The class label estimation of three optimization learners, namely random forest, XGBoost API optimization, and SVM optimization model, is integrated in a stacked architecture. The identification model is validated using data from CHD patients. Finally, in terms of precision, F1, and ROC-Curve, our detection model outperformed existing ones focused on optimization models ensembles and individual classifiers. With random forest optimization, we achieved 90% accuracy, and with the XGBoost API optimization model, we achieved 89% accuracy. In contrast to previous reported research in the existing literature, this analysis indicates that our proposed model makes a substantial contribution.
Similar content being viewed by others
Change history
10 January 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s13204-024-03012-7
References
“Advancing Technology industrialization through intelligent software-google books” [Online]. Available: https://books.google.com.tr/books. [Accessed: 12-Feb-2020].
Alaka SA et al (2020) Functional outcome prediction in ischemic stroke: a comparison of machine learning algorithms and regression models. Front Neurol 11:1–9
Alhanai T, Ghassemi M, Glass J (2018) Detecting depression with audio/text sequence modeling of interviews. Proc Annu Conf Int Speech Commun Assoc 2018:1716–1720
Alhayani BSA, llhan H (2021) Visual sensor intelligent module based image transmission in industrial manufacturing for monitoring and manipulation problems. J Intell Manuf 32:597–610. https://doi.org/10.1007/s10845-020-01590
Alhayani B, Abbas ST, Mohammed HJ et al (2021) Intelligent secured two-way image transmission using corvus corone module over WSN. Wireless Pers Commun. https://doi.org/10.1007/s11277-021-08484-2
Al-Hayani B, Ilhan H (2020) Efficient cooperative image transmission in one-way multi-hop sensor network. Int J Electr Eng Educ 57(4):321–339
Ali L et al (2019) An optimized stacked support vector machines based expert system for the effective prediction of heart failure. IEEE Access 7:54007–54014
Alhayani B, Abdallah AA (2020) Manufacturing intelligent Corvus corone module for a secured two way image transmission under WSN. Eng Comput 38:1751–1788. https://doi.org/10.1108/EC-02-2020-0107
Beunza JJ et al (2019) Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease). J Biomed Inform 97:103257
Ganteng C,“Cardiovascular study dataset|Kaggle.” [Online]. Available: https://www.kaggle.com/christofel04/cardiovascular-study-dataset-predict-heart-disease. [Accessed: 28-Feb-2021]
Gautheron L, Habrard A, Morvant E, Sebban M (2019) Metric learning from imbalanced data. Proc Int Conf Tools Artif Intell ICTAI 2019(9):923–930
Grewal M, Srivastava MM, Kumar P, Varadarajan S (2018) RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans. Proc Int Symp Biomed Imaging 2018:281–284
Han D et al (2020) Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches. J Cardiovasc Comput Tomogr 14(2):168–176
Hasan HS, Alhayani B et al (2021) Novel unilateral dental expander appliance (udex): a compound innovative materials. Comput Mat Continua 68(3):3499–3511. https://doi.org/10.32604/cmc.2021.015968
Hutter F (2014) Meta-learning, 498.
Joloudari JH et al (2020) Coronary artery disease diagnosis ranking the significant features using a random trees model. Int J Environ Res Public Health 17(3):1–24
Karagoz I (2019) “Prediction of heart diseases using majority voting ensemble method. Cmbebih 2019,” IFMBE Proc-C, 73: 159–163.
Kwekha-Rashid AS, Abduljabbar HN, Alhayani B (2021) Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Appl Nanosci. https://doi.org/10.1007/s13204-021-01868-7
Li Z et al (2017) “Thoracic disease identification and localization with limited supervision,” arXiv, pp. 8290–8299
Li L, Jamieson K, DeSalvo G, Rostamizadeh A, Talwalkar A (2018) Hyperband: a novel bandit-based approach to hyperparameter optimization. J Mach Learn Res 18:1–52
Nilashi M et al (2020) Coronary heart disease diagnosis through self-organizing map and fuzzy support vector machine with incremental updates. Int J Fuzzy Syst 22(4):1376–1388
Orphanou K, Dagliati A, Sacchi L, Stassopoulou A, Keravnou E, Bellazzi R (2018) Incorporating repeating temporal association rules in Naïve Bayes classifiers for coronary heart disease diagnosis. J Biomed Inform 81:74–82
Putatunda S and Rama K (2018) “A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost,” ACM Int Conf Proc Ser, pp. 6–10
Rajpurkar P et al (2017) “CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning,” arXiv, pp. 3–9
Rajpurkar P, Hannun AY, Haghpanahi M, Bourn C, Ng AY (2017) “Cardiologist-level arrhythmia detection with convolutional neural networks,” arXiv
Rojas-Dominguez A, Padierna LC, Carpio Valadez JM, Puga-Soberanes HJ, Fraire HJ (2017) Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis. IEEE Access 6:7164–7176
Schlegel B, Sick B (2017) Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces. 2016 IEEE Symp Ser Comput Intell SSCI 2016, no. Section V, 2017.
Siji George CG, Sumathi B (2020) Grid search tuning of hyperparameters in random forest classifier for customer feedback sentiment prediction. Int J Adv Comput Sci Appl 11(9):173–178
Smith MD, Coleman-Jensen A (2020) Food insecurity, acculturation and diagnosis of CHD and related health outcomes among immigrant adults in the USA. Public Health Nutr 23(3):416–431
Sun D, Wen H, Wang D, Xu J (2020) A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology 362:107201
Tama BA, Im S, Lee S (2020) Improving an intelligent detection system for coronary heart disease using a two-tier classifier ensemble. Biomed Res Int 2020:1–10
Ting DSW et al (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22):2211–2223
Wu J, Chen XY, Zhang H, Xiong LD, Lei H, Deng SH (2019) Hyperparameter optimization for machine learning models based on Bayesian optimization. J Electron Sci Technol 17(1):26–40
Yahya W, Ziming K, Juan W et al (2021) Study the influence of using guide vanes blades on the performance of cross-flow wind turbine. Appl Nanosci. https://doi.org/10.1007/s13204-021-01918-0
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There are no conflicts of interest. This study was self-funded. All authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s13204-024-03012-7
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
Alsafi, H.E.S., Ocan, O.N. RETRACTED ARTICLE: A novel intelligent machine learning system for coronary heart disease diagnosis. Appl Nanosci 13, 2473–2480 (2023). https://doi.org/10.1007/s13204-021-01992-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13204-021-01992-4