Cardiovascular Disease Prognosis and Analysis Using Machine Learning Techniques | SpringerLink
Skip to main content
Springer Nature Link
Log in

Cardiovascular Disease Prognosis and Analysis Using Machine Learning Techniques

  • Conference paper
  • First Online:
Advanced Communication and Intelligent Systems (ICACIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1749))

  • 693 Accesses

Abstract

Cardiovascular disease (CVD), includes a number of conditions that affect the heart and, in recent decades, has been the leading cause of death worldwide. Heart disease is linked to a variety of dangers, making it urgently necessary to find precise, reliable, and reasonable ways to make an early diagnosis and start treating the condition. Early diagnosis of cardiovascular illnesses can assist high-risk individuals in deciding on lifestyle changes that will minimise issues, which can be a big medical advancement. Since it takes more intelligence, time and expertise to provide 24-h medical consultations for patients, it is not always possible to accurately monitor patients every day. While an incorrect diagnosis of CVD can be catastrophic, an accurate diagnosis can lower the chance of major health issues. In order to compare the findings and analysis, various machine learning methods and deep learning are used.

Data analysis is a frequently used technique for analysing enormous amounts of data in the healthcare industry. In order to help healthcare professionals forecast heart illness, researchers analyse enormous volumes of complex health records utilising various statistical and machine learning (ML) techniques.

The main objective is to identify an appropriate method for heart disease prediction that is effective and precise. In this chapter, we conducted research on heart disease from the perspective of data analytics. To identify and anticipate the patterns of diseases, we applied different data analytical techniques on data sets of various sizes. We determined which algorithms were the most pertinent and also examined the accuracy, sensitivity, specificity and precision rate of various algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 13727
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 17159
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Dey, D., et al.: Artificial intelligence in cardiovascular Imaging: JACC state-of-the-art review. J. Am. Coll. Cardiol. 73(11), 1317–1335 (2019). https://doi.org/10.1016/j.jacc.2018.12.054. PMID: 30898208; PMCID: PMC6474254

    Article  Google Scholar 

  2. Seetharam, K., Shrestha, S., Sengupta, P.P.: Cardiovascular imaging and intervention through the lens of artificial intelligence. Interv. Cardiol. 16, e31 (2021). https://doi.org/10.15420/icr.2020.04. PMID: 34754333; PMCID: PMC8559149

  3. Friedrich, S., et al.: Applications of artificial intelligence/machine learning approaches in cardiovascular medicine: a systematic review with recommendations. Eur. Heart J. Digit. Health 2(3), 424–436 (2021). https://doi.org/10.1093/ehjdh/ztab054

    Article  Google Scholar 

  4. Benjamins, J.W., Hendriks, T., Knuuti, J., Juarez-Orozco, L.E., van der Harst, P.: A primer in artificial intelligence in cardiovascular medicine. Neth. Hear. J. 27(9), 392–402 (2019). https://doi.org/10.1007/s12471-019-1286-6

    Article  Google Scholar 

  5. Anju, S., et al.: Discovering patterns of cardiovascular disease and diabetes in myocardial infarction patients using association rule mining. https://doi.org/10.20473/fmi.v58i3.34975. eISSN: 2599-056x

  6. Kilic, A.: Artificial intelligence and machine learning in cardiovascular health care. Ann Thorac Surg. 109(5), 1323–1329 (2020). https://doi.org/10.1016/j.athoracsur.2019.09.042. PMID: 31706869

    Article  Google Scholar 

  7. Koulaouzidis, G., Jadczyk, T., Iakovidis, D.K., Koulaouzidis, A., Bisnaire, M., Charisopoulou, D.: Artificial intelligence in cardiology-a narrative review of current status. J. Clin. Med. 11(13), 3910 (2022). https://doi.org/10.3390/jcm11133910. PMID: 35807195; PMCID: PMC9267740

    Article  Google Scholar 

  8. Alam, M.S., Jalil, S.Z.A., Upreti, K.: Analyzing recognition of EEG based human attention and emotion using machine learning. Mater. Today Proc. 56, Part 6, 3349–3354 (2022). https://doi.org/10.1016/j.matpr.2021.10.190. ISSN 2214-7853

  9. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., Kitai, T.: Artificial intelligence in precision cardiovascular medicine. J. Am. Coll. Cardiol. 69(21), 2657–2664 (2017). https://doi.org/10.1016/j.jacc.2017.03.571. PMID: 28545640

    Article  Google Scholar 

  10. Seetharam, K., Shrestha, S., Sengupta, P.P.: Artificial intelligence in cardiovascular medicine. Curr. Treat. Options Cardiovasc. Med. 21(5), 1–14 (2019). https://doi.org/10.1007/s11936-019-0728-1

    Article  Google Scholar 

  11. Upreti, K., Singh, U.K., Jain, R., Kaur, K., Sharma, A.K.: Fuzzy logic based support vector regression (SVR) model for software cost estimation using machine learning. In: Tuba, M., Akashe, S., Joshi, A. (eds.) ICT Systems and Sustainability. LNNS, vol. 321, pp. 917–927. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-5987-4_90

    Chapter  Google Scholar 

  12. Haq, I.U., Chhatwal, K., Sanaka, K., Xu, B.: Artificial intelligence in cardiovascular medicine: current insights and future prospects. Vasc Health Risk Manag. 12(18), 517–528 (2022). https://doi.org/10.2147/VHRM.S279337. PMID: 35855754; PMCID: PMC9288176

    Article  Google Scholar 

  13. Faizal, A.S.M., Malathi Thevarajah, T., Khor, S.M., Chang, S.-W.: A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. Comput. Methods Program. Biomed. 207, 106190 (2021). https://doi.org/10.1016/j.cmpb.2021.106190. ISSN: 0169-2607

  14. 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. PMID: 29880128

    Article  Google Scholar 

  15. Abdolmanafi, A., Duong, L., Dahdah, N., Cheriet, F.: Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. Biomed Opt Express. 8(2), 1203–1220 (2017). https://doi.org/10.1364/BOE.8.001203. PMID: 28271012; PMCID: PMC5330543

    Article  Google Scholar 

  16. Li, Y.: Reinforcement learning applications, 19 August 2019. https://arxiv.org/abs/1908.06973

  17. Haider, M., Upreti, K., Nasir, M., Alam, M., Sharma, A.K.: Addressing image and Poisson noise deconvolution problem using deep learning approaches. Comput. Intell. (2022). https://doi.org/10.1111/coin.12510

    Article  Google Scholar 

  18. Dilsizian, M.E., Siegel, E.L.: Machine meets biology: a primer on artificial intelligence in cardiology and cardiac imaging. Curr. Cardiol. Rep. 20(12), 1–7 (2018). https://doi.org/10.1007/s11886-018-1074-8

    Article  Google Scholar 

  19. Webb, G., Boughton, J., Wang, Z.: Not so Naive Bayes: aggregating one-dependence estimators. Mach. Learn. 58, 5–24 (2005). https://doi.org/10.1007/s10994-005-4258-6

    Article  MATH  Google Scholar 

  20. Berikol, G.B., Yildiz, O., Özcan, İT.: Diagnosis of acute coronary syndrome with a support vector machine. J. Med. Syst. 40(4), 1–8 (2016). https://doi.org/10.1007/s10916-016-0432-6

    Article  Google Scholar 

  21. Palm, M., Parija, S.: Prediction of heart diseases using random forest. J. Phys. Conf. Ser. 1817, 012009 (2021)

    Article  Google Scholar 

  22. Chakraborty, A., Chatterjee, S., Majumder, K., Shaw, R.N., Ghosh, A.: A comparative study of myocardial infarction detection from ECG data using machine learning. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds.) Advanced Computing and Intelligent Technologies. LNNS, vol. 218, pp. 257–267. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2164-2_21

    Chapter  Google Scholar 

  23. Al-Mallah, M.H., et al.: Using machine learning to define the association between cardiorespiratory fitness and all-cause mortality (from the henry ford exercise testing project). Am. J. Cardiol. 120(11), 2078–2084 (2017). https://doi.org/10.1016/j.amjcard.2017.08.029. PMID: 28951020

    Article  Google Scholar 

  24. Saqib Nawaz, M., Shoaib, B., Ashraf, M.A.: Intelligent cardiovascular disease prediction empowered with gradient descent optimization, Heliyon 7(5), e06948 (2021). https://doi.org/10.1016/j.heliyon.2021.e06948. ISSN: 2405–8440

  25. Muhammad, L.J., Al-Shourbaji, I., Haruna, A.A., Mohammed, I.A., Ahmad, A., Jibrin, M.B.: Machine learning predictive models for coronary artery disease. SN Comput. Sci. 2(5), 1–11 (2021). https://doi.org/10.1007/s42979-021-00731-4

    Article  Google Scholar 

  26. Ghosh, P., et al.: Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques. IEEE Access 9, 19304–19326 (2021). https://doi.org/10.1109/ACCESS.2021.3053759

    Article  Google Scholar 

  27. Stuckey, T.D., et al.: Cardiac Phase Space Tomography: a novel method of assessing coronary artery disease utilizing machine learning. PLoS ONE 13(8), e0198603 (2018). https://doi.org/10.1371/journal.pone.0198603. PMID: 30089110; PMCID: PMC6082503

    Article  Google Scholar 

  28. Kanwal, S., Rashid, J., Nisar, M.W., Kim, J., Hussain, A.: An effective classification algorithm for heart disease prediction with genetic algorithm for feature selection. In: 2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC), pp. 1–6 (2021). https://doi.org/10.1109/MAJICC53071.2021.9526242

  29. Cherian, V., Bindu, M.S.: Heart disease prediction using naïve Bayes algorithm and Laplace smoothing technique. Int. J. Comput. Sci. Trends Technol. (IJCST) 5(2), 68–73 (2017)

    Google Scholar 

  30. Upreti, K., et al.: Prediction of mechanical strength by using an artificial neural network and random forest algorithm. J. Nanomater. 2022 (2022). https://doi.org/10.1155/2022/7791582

  31. Souza Filho, E.M., et al.: Artificial intelligence in cardiology: concepts, tools and challenges - “The Horse is the One Who Runs, You Must Be the Jockey”. Arq Bras Cardiol. 114(4), 718–725 (2020). https://doi.org/10.36660/abc.20180431. English, Portuguese. PMID: 32491009

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, Delhi, India

    Anmol Kapoor

  2. Department of Computer Science and Engineering, Akhilesh Das Gupta Institute of Technology and Management, Delhi, India

    Shreya Kapoor & Kamal Upreti

  3. Department of Computer Science and Engineering, Sunder Deep Engineering College, Ghaziabad, India

    Prashant Singh

  4. Department of Science, Mira Model School, Delhi, India

    Seema Kapoor

  5. Department of Computer Science, College of Computer Science and IT, Jazan University, Jizan, Saudi Arabia

    Mohammad Shabbir Alam & Mohammad Shahnawaz Nasir

Authors
  1. Anmol Kapoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shreya Kapoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kamal Upreti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Prashant Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seema Kapoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohammad Shabbir Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mohammad Shahnawaz Nasir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kamal Upreti .

Editor information

Editors and Affiliations

  1. Bharath Institute of Higher Education and Research, Chennai, India

    Rabindra Nath Shaw

  2. Systems Research Institute, Warsaw, Poland

    Marcin Paprzycki

  3. The Neotia University, Sarisha, India

    Ankush Ghosh

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kapoor, A. et al. (2023). Cardiovascular Disease Prognosis and Analysis Using Machine Learning Techniques. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol 1749. Springer, Cham. https://doi.org/10.1007/978-3-031-25088-0_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-25088-0_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25087-3

  • Online ISBN: 978-3-031-25088-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics