Skip to main content
SpringerLink
Log in

Machine Learning Method for Analyzing and Predicting Cardiovascular Disease

  • Conference paper
  • First Online:
Machine Intelligence for Research and Innovations (MAiTRI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 832))

  • 40 Accesses

Abstract

Heart Disease is among the leading causes of mortality globally. Heart disease is responsible for delivering plasma to every portion of the body. Frequent causes of cardiac arrest are ischemic heart disease (CAD) and congestive heart failure (CHF). Traditional medical techniques (such as angiography) have higher costs and significant health risks and are used to diagnose heart disease. Therefore, scientists have developed a number of robotic detection methods employing ML algorithms and knowledge discovery techniques. ML-based computer-aided diagnostic techniques make detecting cardiovascular disease simple, efficient, and trustworthy. In the past, multiple machine learning, data gathering, and information sources have been utilized. In numerous past evaluations, several research articles devoted to a specified data format have been released. Likewise, the purpose of this work is to conduct a comprehensive analysis of computerized diagnostics for cardiovascular disease prognosis using multiple techniques.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • 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. Ahlawat V, Thakur R, Narayan Y (2018) Support vector machine based classification improvement for EMG signals using principal component analysis. J Eng Appl Sci 13(8):6341–6345

    Google Scholar 

  2. Redie DK, Sirko AE, Demissie TM, Teferi SS, Shrivastava VK, Verma OP, Sharma TK (2023) Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model. Evol Intel 16(3):729–738

    Article  Google Scholar 

  3. Narayan Y, Kumari M, Rajan R (2022) SEMG signals identification using DT And LR classifier by wavelet-based features. Int J Electr Electron Res 10(4):822–825. https://doi.org/10.37391/IJEER.100410

    Article  Google Scholar 

  4. Godfrey KM, Juarascio A, Manasse S, Minassian A, Risbrough V, Afari N (2019) Heart rate variability and emotion regulation among individuals with obesity and loss of control eating. Physiol Behav 199:73–78. https://doi.org/10.1016/j.physbeh.2018.11.009

    Article  Google Scholar 

  5. Mohi Uddin KM, Ripa R, Yeasmin N, Biswas N, Dey SK (2023) Machine learning-based approach to the diagnosis of cardiovascular vascular disease using a combined dataset. Intell Based Med 7:100100. https://doi.org/10.1016/j.ibmed.2023.100100

  6. Swathy M, Saruladha K (2022) A comparative study of classification and prediction of cardio-vascular diseases (CVD) using machine learning and deep learning techniques. ICT Express 8(1):109–116. https://doi.org/10.1016/j.icte.2021.08.021

    Article  Google Scholar 

  7. Narayan Y (2021) Analysis of MLP and DSLVQ classifiers for EEG signals based movements identification. In: 2021 2nd Global conference for advancement in technology, GCAT 2021. Institute of Electrical and Electronics Engineers Inc., https://doi.org/10.1109/GCAT52182.2021.9587868

  8. Li Q, Campan A, Ren A, Eid WE (2022) Automating and improving cardiovascular disease prediction using Machine learning and EMR data features from a regional healthcare system. Int J Med Inform 163. https://doi.org/10.1016/j.ijmedinf.2022.104786

  9. Narayan Y, Mathew L, Chatterji S (2018) SEMG signal classification with novel feature extraction using different machine learning approaches. J Intell Fuzzy Syst 35(5):5099–5109. https://doi.org/10.3233/JIFS-169794

    Article  Google Scholar 

  10. El-Hasnony IM, Elzeki OM, Alshehri A, Salem H (2022) Multi-label active learning-based machine learning model for heart disease prediction. Sensors 22(3). https://doi.org/10.3390/s22031184

  11. Ansarullah SI, Saif SM, Kumar P, Kirmani MM (2022) Significance of visible non-invasive risk attributes for the initial prediction of heart disease using different machine learning techniques. Comput Intell Neurosci 2022:9580896. https://doi.org/10.1155/2022/9580896

    Article  Google Scholar 

  12. Kavitha M, Gnaneswar G, Dinesh R, Sai YR, Suraj RS (2021) Heart disease prediction using hybrid machine learning model. In: 2021 6th International conference on inventive computation technologies (ICICT). pp 1329–1333. https://doi.org/10.1109/ICICT50816.2021.9358597

  13. El-Shafiey MG, Hagag A, El-Dahshan ESA, Ismail MA (2022) A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest. Multimed Tools Appl 81(13):18155–18179. https://doi.org/10.1007/s11042-022-12425-x

    Article  Google Scholar 

  14. Riyaz L, Butt MA, Zaman M, Ayob O (2022) Heart disease prediction using machine learning techniques: a quantitative review. In: International conference on innovative computing and communications. Springer Singapore, Singapore, pp 81–94

    Google Scholar 

  15. Nagarajan SM, Muthukumaran V, Murugesan R, Joseph RB, Meram M, Prathik A (2022) Innovative feature selection and classification model for heart disease prediction. J Reliab Intell Environ 8(4):333–343. https://doi.org/10.1007/s40860-021-00152-3

    Article  Google Scholar 

  16. Abu-Alhaija M, Turab NM (2022) Automated learning of ECG streaming data through machine learning internet of things. Intell Autom Soft Comp 32(1)

    Google Scholar 

  17. Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P (2021) Prediction of heart disease using a combination of machine learning and deep learning. Comput Intell Neurosci 2021. https://doi.org/10.1155/2021/8387680

  18. Almazroi AA (2022) Survival prediction among heart patients using machine learning techniques. Math Biosci Eng 19(1):134–145. https://doi.org/10.3934/mbe.2022007

    Article  Google Scholar 

  19. Ayon SI, Islam MM, Hossain MR (2022) Coronary artery heart disease prediction: a comparative study of computational intelligence techniques. IETE J Res 68(4):2488–2507. https://doi.org/10.1080/03772063.2020.1713916

    Article  Google Scholar 

  20. Narayan Y (2021) Direct comparison of SVM and LR classifier for SEMG signal classification using TFD features. Mater Today Proc 45:3543–3546

    Article  Google Scholar 

  21. Kirar A, Bhalerao S, Verma OP, Ansari IA (2022) Protecting ECG signals with hybrid swarm intelligence algorithm. In: Garg Lalit BC, Basterrech STK (eds) Artificial intelligence in healthcare. Springer Singapore, Singapore, pp 47–60. https://doi.org/10.1007/978-981-16-6265-2_4

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Chandigarh University, Mohali, Punjab, India

    Yogendra Narayan & Mandeep Kaur Ghumman

  2. Department of CSE, Chandigarh University, Mohali, Punjab, India

    Charanjeet Gaba

Authors
  1. Yogendra Narayan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mandeep Kaur Ghumman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Charanjeet Gaba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yogendra Narayan .

Editor information

Editors and Affiliations

  1. Dept of Instrumentation and Control Engg, Dr. B. R. Ambedkar National Institute of, Jalandhar, Punjab, India

    Om Prakash Verma

  2. School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Department of Electrical Engineering, Malaviya National Institute of Technolog, Jaipur, Rajasthan, India

    Rajesh Kumar

  4. Department of Mathematics, DR BR Ambedkar NIT Jalandhar, Jalandhar, Punjab, India

    Anupam Yadav

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Narayan, Y., Kaur Ghumman, M., Gaba, C. (2024). Machine Learning Method for Analyzing and Predicting Cardiovascular Disease. In: Verma, O.P., Wang, L., Kumar, R., Yadav, A. (eds) Machine Intelligence for Research and Innovations. MAiTRI 2023. Lecture Notes in Networks and Systems, vol 832. Springer, Singapore. https://doi.org/10.1007/978-981-99-8129-8_11

Download citation

Publish with us

Policies and ethics

Search

Navigation