Skip to main content

Advertisement

SpringerLink
Log in

A Comprehensive Analysis of Hypertension Disease Risk-Factors, Diagnostics, and Detections Using Deep Learning-Based Approaches

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

High blood pressure, often known as hypertension, is a common and possibly fatal disorder that affects a large section of the world’s population. For complications to be avoided and the risk of cardiovascular diseases to be decreased, early detection and control of hypertension are essential. By examining numerous physiological and clinical data, deep learning models have shown the potential in assisting in the identification of hypertension. The aim of the paper is to explore the application of deep learning-based approaches to building an automated system for hypertension detection. A diverse dataset comprising blood pressure measurements, demographic information, medical history, and lifestyle factors is utilized. Different deep learning models, including Gated Recurrent Unit, Embedded GRU, Bidirectional GRU, Long Short-Term Memory, and their different versions are employed to build predictive models for hypertension detection along with stroke and heart-disease prediction. Pre-processing is done on the applied dataset, which has many different features for predicting hypertension, dealing with missing values, normalizing features, and dealing with class imbalance. Techniques for feature selection are used to determine which variables are most useful for predicting hypertension. The outcomes show that hypertension can be accurately detected using models that have been implemented. The best performance is shown by bidirectional GRU, which detects hypertension with an accuracy of 99.68% and an F1-score, precision, and recall of 0.99. While Embedded GRU produced the greatest results, such as 98.10% to predict heart disease, Bidirectional LSTM also yields the finest outcomes for stroke prediction with an accuracy of 98.85%. The used models perform better than conventional diagnostic methods and display encouraging outcomes. By integrating these models into healthcare systems, it may be likely to classify people at high risk for developing hypertension early and to support prompt therapies, which will improve patient outcomes and lower the financial burden on the healthcare system.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data Availability

Not applicable.

References

  1. Murray CJL et al (2020) Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet 396(10258):1223–1249. https://doi.org/10.1016/s0140-6736(20)30752-2

    Article  Google Scholar 

  2. Parati G et al (2021) Home blood pressure monitoring: methodology, clinical relevance and practical application: a 2021 position paper by the Working Group on Blood Pressure Monitoring and Cardiovascular Variability of the European Society of Hypertension. J Hypertens 39(9):1742–1767. https://doi.org/10.1097/hjh.0000000000002922

    Article  Google Scholar 

  3. Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7

    Article  Google Scholar 

  4. Beaney T et al (2019) May Measurement Month 2018: a pragmatic global screening campaign to raise awareness of blood pressure by the International Society of Hypertension. Eur Heart J 40(25):2006–2017. https://doi.org/10.1093/eurheartj/ehz300

    Article  Google Scholar 

  5. Krum H, Jelinek MV, Stewart S, Sindone A, Atherton JJ, Hawkes AL (2006) Guidelines for the prevention, detection and management of people with chronic heart failure in Australia 2006. Med J Aust 185(10):549–556. https://doi.org/10.5694/j.1326-5377.2006.tb00690.x

    Article  Google Scholar 

  6. Silva GFS, Fagundes TP, Teixeira BC, ChiavegattoFilho ADP (2022) Machine learning for hypertension prediction: a systematic review. Curr Hypertens Rep 24(11):523–533. https://doi.org/10.1007/s11906-022-01212-6

    Article  Google Scholar 

  7. Rajpurkar P, Chen E, Banerjee O, Topol EJ (2022) AI in health and medicine. Nat Med 28(1):31–38. https://doi.org/10.1038/s41591-021-01614-0

    Article  Google Scholar 

  8. Yang Z et al (2020) Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thorac Dis 12(3):165–174. https://doi.org/10.21037/jtd.2020.02.64

    Article  Google Scholar 

  9. Alaa AM, Bolton T, Di Angelantonio E, Rudd JHF, van der Schaar M (2019) Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants. PLoS ONE 14(5):e0213653. https://doi.org/10.1371/journal.pone.0213653

    Article  Google Scholar 

  10. Maniruzzaman Md, Rahman MdJ, Ahammed B, Abedin MdM (2020) Classification and prediction of diabetes disease using machine learning paradigm. Health Inf Sci Syst. https://doi.org/10.1007/s13755-019-0095-z

    Article  Google Scholar 

  11. Jackins V, Vimal S, Kaliappan M, Lee MY (2020) AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes. J Supercomput 77(5):5198–5219. https://doi.org/10.1007/s11227-020-03481-x

    Article  Google Scholar 

  12. Aycheh HM et al (2018) Biological brain age prediction using cortical thickness data: a large scale cohort study. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2018.00252

    Article  Google Scholar 

  13. Montagna S et al (2022) Machine learning in hypertension detection: a study on world hypertension day data. J Med Syst. https://doi.org/10.1007/s10916-022-01900-5

    Article  Google Scholar 

  14. Chaplot N, Pandey D, Kumar Y, Sisodia PS (2023) A comprehensive analysis of artificial intelligence techniques for the prediction and prognosis of genetic disorders using various gene disorders. Arch Comput Methods Eng 30(5):3301–3323. https://doi.org/10.1007/s11831-023-09904-1

    Article  Google Scholar 

  15. Kanna GP, Kumar SJKJ, Parthasarathi P, Kumar Y (2023) A review on prediction and prognosis of the prostate cancer and Gleason grading of prostatic carcinoma using deep transfer learning based approaches. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-023-09896-y

    Article  Google Scholar 

  16. Saitoh S (2018) A1570 comparison of hypertension prediction model. J Hypertens 36:e263. https://doi.org/10.1097/01.hjh.0000549072.55475.e0

    Article  Google Scholar 

  17. Weidong J, Zhang Y, Cheng Y, Wang Y, Zhou Y (2022) Development and validation of prediction models for hypertension risks: a cross-sectional study based on 4,287,407 participants. Front Cardiovasc Med. https://doi.org/10.3389/fcvm.2022.928948

  18. Wu J-H et al (2019) Risk assessment of hypertension in steel workers based on LVQ and Fisher-SVM deep excavation. IEEE Access 7:23109–23119. https://doi.org/10.1109/access.2019.2899625

    Article  Google Scholar 

  19. Abrar S, Loo CK, Kubota N (2021) A multi-agent approach for personalized hypertension risk prediction. IEEE Access 9:75090–75106. https://doi.org/10.1109/access.2021.3074791

    Article  Google Scholar 

  20. Kurniawan R et al (2023) Hypertension prediction using machine learning algorithm among Indonesian adults. IAES Int J Artif Intell 12(2):776. https://doi.org/10.11591/ijai.v12.i2.pp776-784

    Article  Google Scholar 

  21. Chen J, Chen Y, Li J, Wang J, Lin Z, Nandi AK (2022) Stroke risk prediction with hybrid deep transfer learning framework. IEEE J Biomed Health Inform 26(1):411–422. https://doi.org/10.1109/jbhi.2021.3088750

    Article  Google Scholar 

  22. Chen S et al (2023) Hypertension monitoring by a real time management system for patients in community and its data mining by vector autoregressive model. IEEE Access 11:12607–12622. https://doi.org/10.1109/access.2023.3240084

    Article  Google Scholar 

  23. Ambika M, Raghuraman G, SaiRamesh L (2020) Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques. Soft Comput 24(17):13293–13304. https://doi.org/10.1007/s00500-020-04743-9

    Article  Google Scholar 

  24. Chai SS, Goh KL, Cheah WL, Chang YHR, Ng GW (2022) Hypertension prediction in adolescents using anthropometric measurements: do machine learning models perform equally well? Appl Sci 12(3):1600. https://doi.org/10.3390/app12031600

    Article  Google Scholar 

  25. Fitriyani NL, Syafrudin M, Alfian G, Rhee J (2019) Development of disease prediction model based on ensemble learning approach for diabetes and hypertension. IEEE Access 7:144777–144789. https://doi.org/10.1109/access.2019.2945129

    Article  Google Scholar 

  26. Fang M et al (2021) A hybrid machine learning approach for hypertension risk prediction. Neural Comput Appl 35(20):14487–14497. https://doi.org/10.1007/s00521-021-06060-0

    Article  Google Scholar 

  27. Zhao H et al (2021) Predicting the risk of hypertension based on several easy-to-collect risk factors: a machine learning method. Front Public Health. https://doi.org/10.3389/fpubh.2021.619429

    Article  Google Scholar 

  28. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  29. Zhou C, Sun C, Liu Z, Lau FCM (2015) A C-LSTM neural network for text classification. arXiv. https://doi.org/10.48550/ARXIV.1511.08630

  30. Sak H, Senior A, Beaufays F (2014) Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv. https://doi.org/10.48550/ARXIV.1402.1128

  31. Graves A, Jaitly N, Mohamed A (2013) Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE workshop on automatic speech recognition and understanding. IEEE. https://doi.org/10.1109/asru.2013.6707742

  32. Alzubaidi L et al (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. https://doi.org/10.1186/s40537-021-00444-8

    Article  Google Scholar 

  33. Islam MdZ, Islam MdM, Asraf A (2020) A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inform Med Unlocked 20:100412. https://doi.org/10.1016/j.imu.2020.100412

    Article  Google Scholar 

  34. Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput 31(7):1235–1270. https://doi.org/10.1162/neco_a_01199

    Article  MathSciNet  Google Scholar 

  35. Agarap AF (2017) A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data. arXiv. https://doi.org/10.48550/ARXIV.1709.03082

  36. Li X, Yuan A, Lu X (2018) Multi-modal gated recurrent units for image description. Multimed Tools Appl 77(22):29847–29869. https://doi.org/10.1007/s11042-018-5856-1

    Article  Google Scholar 

  37. Zhou G-B, Wu J, Zhang C-L, Zhou Z-H (2016) Minimal gated unit for recurrent neural networks. Int J Autom Comput 13(3):226–234. https://doi.org/10.1007/s11633-016-1006-2

    Article  Google Scholar 

  38. Demir F (2021) DeepCoroNet: a deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images. Appl Soft Comput 103:107160. https://doi.org/10.1016/j.asoc.2021.107160

    Article  Google Scholar 

  39. Lynn HM, Pan SB, Kim P (2019) A deep bidirectional GRU network model for biometric electrocardiogram classification based on recurrent neural networks. IEEE Access 7:145395–145405. https://doi.org/10.1109/access.2019.2939947

    Article  Google Scholar 

  40. Acharya UR et al (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396. https://doi.org/10.1016/j.compbiomed.2017.08.022

    Article  Google Scholar 

  41. Al-Hassan A, Al-Dossari H (2021) Detection of hate speech in Arabic tweets using deep learning. Multimed Syst 28(6):1963–1974. https://doi.org/10.1007/s00530-020-00742-w

    Article  Google Scholar 

  42. Kumar Y, Koul A, Mahajan S (2022) A deep learning approaches and fastai text classification to predict 25 medical diseases from medical speech utterances, transcription and intent. Soft Comput 26(17):8253–8272. https://doi.org/10.1007/s00500-022-07261-y

    Article  Google Scholar 

  43. Singh J, Sandhu JK, Kumar Y (2023) An analysis of detection and diagnosis of different classes of skin diseases using artificial intelligence-based learning approaches with hyper parameters. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-023-10005-2

    Article  Google Scholar 

  44. Kumar A, Kumar N, Kuriakose J et al (2023) A review of deep learning-based approaches for detection and diagnosis of diverse classes of drugs. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-023-09936-7

    Article  Google Scholar 

  45. Bhardwaj P, Kumar S, Kumar Y (2023) A comprehensive analysis of deep learning-based approaches for the prediction of gastrointestinal diseases using multi-class endoscopy images. Arch Comput Methods Eng 30:4499–4516. https://doi.org/10.1007/s11831-023-09951-8

    Article  Google Scholar 

  46. Modi K, Singh I, Kumar Y (2023) A comprehensive analysis of artificial intelligence techniques for the prediction and prognosis of lifestyle diseases. Arch Comput Methods Eng 30:4733–4756. https://doi.org/10.1007/s11831-023-09957-2

    Article  Google Scholar 

  47. Thakur K, Kaur M, Kumar Y (2023) A comprehensive analysis of deep learning-based approaches for prediction and prognosis of infectious diseases. Arch Comput Methods Eng 30:4477–4497. https://doi.org/10.1007/s11831-023-09952-7

    Article  Google Scholar 

  48. Kumar Y, Kaur I, Mishra S (2023) Foodborne disease symptoms, diagnostics, and predictions using artificial intelligence-based learning approaches: a systematic review. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-023-09991-0

    Article  Google Scholar 

  49. Kaur K, Singh C, Kumar Y (2023) Diagnosis and detection of congenital diseases in new-borns or fetuses using artificial intelligence techniques: a systematic review. Arch Comput Methods Eng 30:3031–3058. https://doi.org/10.1007/s11831-023-09892-2

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of CSE, Desh Bhagat University, Mandi Gobindgarh, Punjab, India

    Simranjit Kaur & Khushboo Bansal

  2. Department of CSE, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Yogesh Kumar

  3. Department of ICT, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Ankur Changela

Authors
  1. Simranjit Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khushboo Bansal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yogesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ankur Changela
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankur Changela.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, S., Bansal, K., Kumar, Y. et al. A Comprehensive Analysis of Hypertension Disease Risk-Factors, Diagnostics, and Detections Using Deep Learning-Based Approaches. Arch Computat Methods Eng 31, 1939–1958 (2024). https://doi.org/10.1007/s11831-023-10035-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-023-10035-w

Search

Navigation