AccScience Publishing / AIH / Volume 1 / Issue 1 / DOI: 10.36922/aih.1746
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ORIGINAL RESEARCH ARTICLE

Artificial intelligence model for prediction of cardiovascular disease: An empirical study

Buhari Ugbede Umar1 ,  Lukman Adewale Ajao1* ,  Eustace Mananyi Dogo1 ,  Falilat Jumoke Ajao2 ,  Micheal Atama1
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1 Department of Computer Engineering, Federal University Technology, Minna, Niger State, Nigeria
2 Department of Computer Science, Kwara State University, Malete-Ilorin, Kwara State, Nigeria
AIH 2024, 1(1), 42–56; https://doi.org/10.36922/aih.1746
Submitted: 1 September 2023 | Accepted: 14 November 2023 | Published: 1 December 2023
© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Cardiovascular disease (CVD) is a disease related to the heart and blood vessels. Prediction of CVD is essential for early detection and diagnosis, which is however compounded by the complex interplay between medical history, physical examination outcomes, and imaging results. While the existing automated systems are fraught with the usage of irrelevant and redundant attributes, artificial intelligence (AI) helps in the identification of potential CVD populations by prediction models. This work aims at developing an AI model for predicting CVD using different classifications of machine learning techniques. The CVD dataset was obtained from the UCI repository containing about 76 cardiac attributes for training in various machine learning models, which include a hybrid of artificial neural network-genetic algorithm (ANN-GA), artificial neural network, support vector machine (SVM), K-means, K-nearest neighbor (KNN), and decision tree (DT). The performance of the models was measured in terms of accuracy, means square error, sensitivity, specificity, and precision. The results showed that the hybrid model of ANN-GA performs better with an accuracy of 86.4%, compared to the SVM, K-means, KNN, and DT measured at 84.0%, 59.6%, 79.0%, and 77.8%, respectively. It was observed that the system performs better as the number of datasets increases in the database, with a fewer selection of attributes using genetic algorithm for selection. Thus, the ANN-GA model is recommended for CVD prediction and diagnosis.

Keywords
Artificial neural network
Cardiovascular disease
Genetic algorithm
Machine learning
Support vector machine
Funding
None.
References
  1. Dinesh KG, Arumugaraj K, Santhosh KD, et al., 2018, Prediction of Cardiovascular Disease Using Machine Learning Algorithms. In: International Conference on Current Trends towards Converging Technologies (ICCTCT). Coimbatore, India, p1–7. https://doi.org/10.1109/ICCTCT.2018.8550857

 

  1. Wong KK, Fortino G, Abbott D, 2020, Deep learning-based cardiovascular image diagnosis: A promising challenge. Future Gener Comput Syst, 110: 802–811. https://doi.org/10.1016/j.future.2019.09.047

 

  1. Masethe HD, Masethe MA, 2014, Prediction of heart disease using classification algorithms. Proc World Congr Eng Comput Sci, 2: 25–29.

 

  1. Crosier R, Austin PC, Ko DT, et al., 2021, Intensity of guideline-directed medical therapy for coronary heart disease and ischemic heart failure outcomes. Am J Med, 134: 672–681. https://doi.org/10.1016/j.amjmed.2020.10.017

 

  1. Jibril IZ, Agajo J, Ajao LA, et al., 2018, Development of a medical expert system for hypertensive patients diagnosis: A knowledge-based rules. Adv Electron Eng J, 1: 23–29.

 

  1. Wang S, Shen B, Martin C, 2021, Signal Analysis of Heart Rate Variability and Applications on the Diagnosis of Cardiovascular Diseases. In: MOL2NET, International Conference Series on Multidisciplinary Sciences, p1–3.

 

  1. Martin-Isla C, Campello VM, Izquierdo C, et al., 2020, Image-based cardiac diagnosis with machine learning: A review. Front Cardiovasc Med, 7: 1–3.

 

  1. Tarawneh M, Embarak O, 2019, Hybrid Approach for Heart Disease Prediction Using Data Mining Techniques. In: International Conference on Emerging Internetworking, Data and Web Technologies, p447–454.

 

  1. Khourdifi Y, Bahaj M, 2018, Applying Best Machine Learning Algorithms for Breast Cancer Prediction and Classification. In: International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), p1–5.

 

  1. Boateng D, Agyemang C, Beune E, et al., 2018, Cardiovascular disease risk prediction in Sub-Saharan African populations comparative analysis of risk algorithms in the RODAM study. Int J Cardiol, 254: 310–315.

 

  1. Alaa AM, Bolton T, Di Angelantonio E, et al., 2019, Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants. PloS One, 14: e0213653. https://doi.org/10.1371/journal.pone.0213653

 

  1. Mohan S, Thirumalai C, Srivastava G, 2019, Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7: 81542–81554. https://doi.org/10.1109/ACCESS.2019.2923707

 

  1. Rubini PE, Subasini CA, Katharine AV, et al., 2021, A cardiovascular disease prediction using machine learning algorithms. Ann Rom Soc Cell Biol, 25: 904–912.

 

  1. Sanchis-Gomar F, Perez-Quilis C, Leischik R, et al., 2016, Epidemiology of coronary heart disease and acute coronary syndrome. Ann Transl Med, 4: 256. https://doi.org/10.21037/atm.2016.06.33

 

  1. Yang L, Wu H, Jin X, et al., 2020, Study of a cardiovascular disease prediction model based on random forest in Eastern China. Sci Rep, 10: 5245. https://doi.org/10.1038/s41598-020-62133-5

 

  1. Patel J, TejalUpadhyay D, Patel S, 2015, Techniques. In: International Conference on Smart Homes and Health Telematics. Cham: Springer, p299–306.

 

  1. Abdeldjouad FZ, Brahami M, Matta N, 2020, A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques. In: International Conference on Smart Homes and Health Telematics, p299–306.

 

  1. Latha CB, Jeeva SC, 2019, Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inf Med Unlocked, 16: 100203. https://doi.org/10.1016/j.imu.2019.100203

 

  1. Subhadra K, Vikas B, 2019, Neural network-based intelligent system for predicting heart disease. Int J Innov Technol Exploring Eng, 8: 484–487.

 

  1. Aliyu A, Ajao LA, Agajo J, et al., 2018, Human vital physiological parameters monitoring: A wireless body area technology based internet of things. J Teknol Sistem Komput (JTSiskom), 6: 115–121. https://doi.org/10.14710/jtsiskom.6.3.2018.115-121

 

  1. Waghulde NP, Patil NP, 2014, Genetic neural approach for heart disease prediction. Int J Adv Comput Res, 4: 778.

 

  1. Ngabo D, Wang D, Iwendi C, et al., 2021, Blockchain-based security mechanism for the medical data at fog computing architecture of internet of things. Electronics, 10: 1–17. https://doi.org/10.3390/electronics10172110

 

  1. Salihu BA, Ajao LA, Audu SA, et al., 2020, Design of Robotic Wearable Shoes for Locomotion Assistance System. In: Information and Communication Technology Application (ICTA 2020), Germany: Springer, p689–702. https://doi.org/10.1007/978-3-030-69143-1_52

 

  1. Kavitha R, Kannan E, 2014, A methodology for heart disease diagnosis using data mining technique. Res J App Sci Eng Technol, 8: 1350–1354. https://doi.org/10.19026/RJASET.8.1106

 

  1. Jindal H, Agrawal S, Khera R, et al., 2021, Heart disease prediction using machine learning algorithms. IOP Conf Ser Mater Sci Eng, 1022: 012072. https://doi.org/10.1088/1757-899X/1022/1/012072

 

  1. Çavuşlar G, Çatay B, Apaydin MS, 2012, A tabu search approach for the NMR protein structure-based assignment problem. IEEE/ACM Trans on Comput Biol Bioinform, 9: 1621–1628. https://doi.org/10.1109/TCBB.2012.122

 

  1. Ateş A, Yeroglu C, 2016, Optimal fractional order PID design via tabu search based algorithm. ISA Trans, 60: 109–118. https://doi.org/10.1016/j.isatra.2015.11.015

 

  1. Diwakar M, Tripathi A, Joshi K, et al., 2021, Latest trends on heart disease prediction using machine learning and image fusion. Mater Today Proc, 37: 3213–3218. https://doi.org/10.1016/j.matpr.2020.09.078

 

  1. Chowdhury MN, Ahmed E, Siddik MA, 2021, Heart disease prognosis using machine learning classification techniques. In: 2021 6th International Conference for Convergence in Technology (I2CT), p1–6. https://doi.org/10.1109/I2CT51068.2021.9418181.

 

  1. Peter TJ, Somasundaram K, 2012, Study and development of novel feature selection framework for heart disease prediction. Int J Sci Res, 2: 1–7.

 

  1. Srivenkatesh M, 2020, Prediction of cardiovascular disease using machine learning algorithms. Int J Eng Adv Technol, 9: 1–7. https://doi.org/10.35940/ijeat.B3986.029320

 

  1. Sharma S, Parmar M, 2020, Heart diseases prediction using deep learning neural network model. Int J Innov Technol Exploring Eng, 9: 2244–2248. https://doi.org/10.35940/ijitee.C9009.019320

 

  1. Mohan S, Thirumalai C, Srivastava G, 2019, Effective heart disease prediction using hybrid machine learning techniques, IEEE Access, 7: 81542–81554. https://doi.org/10.1109/ACCESS.2019.2923707

 

  1. Chowdary GJ, 2020, Effective prediction of cardiovascular disease using cluster of machine learning algorithms. J Crit Rev, 7: 1865–1875.

 

  1. Krittanawong C, Virk HU, Bangalore S, et al., 2020, Machine learning prediction in cardiovascular diseases: A meta-analysis. Sci Rep, 10: 16057. https://doi.org/10.1038/s41598-020-72685-1

 

  1. Rabbi MF, Uddin MP, Ali MA, et al., 2018, Performance evaluation of data mining classification techniques for heart disease prediction. Am J Eng Res, 7: 278–283.

 

  1. Chandralekha M, Shenbagavadivu N, 2018, Performance analysis of various machine learning techniques to predict cardiovascular disease: An empirical study. Appl Math Inf Sci, 12: 217–226.

 

  1. Khourdifi Y, Bahaj M, 2019, Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. Int J Intell Eng Syst, 12: 242–252. https://doi.org/10.22266/ijies2019.0228.24

 

  1. Shah D, Patel S, Bharti SK, 2020, Heart disease prediction using machine learning techniques. SN Comput Sci, 1: 1–6. https://doi.org/10.1007/s42979-020-00365-y

 

  1. Kaur R, Kaur S, 2015, Prediction of heart disease based on risk factors using genetic SVM classifier. Int J Adv Res Comput Sci Softw Eng, 5: 205–208.

 

  1. Haq IU, Chhatwal K, Sanaka K, et al., 2022, Artificial intelligence in cardiovascular medicine: current insights and future prospects. Vasc Health Risk Manag, 18: 517–528. https://doi.org/10.2147/VHRM.S279337

 

  1. Taylan O, Alkabaa AS, Alqabbaa HS, et al., 2023, Early prediction in classification of cardiovascular diseases with machine learning, neuro-fuzzy and statistical methods. Biology (Basel), 12: 117. https://doi.org/10.3390/biology12010117

 

  1. Tran L, Chi L, Bonti A, et al., 2021, Mortality prediction of patients with cardiovascular disease using medical claims data under artificial intelligence architectures: Validation study. JMIR Med Inform, 9: e25000. https://doi.org10.2196/25000

 

  1. Khan A, Qureshi M, Daniyal M, et al., 2023, Novel study on machine learning algorithm-based cardiovascular disease prediction. Health Soc Care Community, 2023: 1406060. https://doi.org/10.1155/2023/1406060

 

  1. Bi H, Lu F, Duan S, et al., 2020, Two-level principal-agent model for schedule risk control of IT outsourcing project based on genetic algorithm. Eng Appl Artif Intell, 91: 103584. https://doi.org/10.1016/j.engappai.2020.103584

 

  1. Ajao LA, Apeh ST, 2023, Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning. Intell Syst Appl, 18: 200216. https://doi.org/10.1016/j.iswa.2023.200216
Conflict of interest
The authors declare that they have no competing interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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