Skip to main content
Springer Nature Link
Log in

Smart wearable model for predicting heart disease using machine learning

Wearable to predict heart risk

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The heart diseases are one of the leading causes of death in today’s world. Wearable Technology is gaining a lot of attention in today’s world. This research focuses on developing a wearable biomedical prototype to predict the presence of heart disease. The research’s findings will be especially helpful in countries where doctor to patient ratio is alarmingly low as wearable technology can be used to monitor parameters of patients anywhere, without restricting to hospital environments. The objective is to predict a possibility of heart disease using Machine Learning Algorithms. Electrocardiogram (ECG) patterns are obtained from the ECG sensor embedded in the wearable biomedical prototype. Variations in ECG patterns are monitored. ECG patterns are used to obtain the heart rate using the R-to-R method. Cleveland data set is used which has 13 attributes including ECG related attributes like resting ECG results, depression in ST-segment induced by exercise relative to rest and slope of peak exercise segment. The proposed system with the Random Forest Algorithm have predicted with an efficiency of 88%. For testing the prototype, human subjects are not involved rather we used static(real) data and the results are sent to the app to take necessary action. This prototype which is developed as a proof of concept will help the elderly people as an assistive equipment.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Adhikari, Das NC, Alka A, Garg R (2017) Hpps: Heart problem prediction system using machine learning. In: Computer science and information technology, SIGI, pp 23–37

  • Al-khafajiy M, Baker T, Chalmers C, Asim M, Kolivand H, Fahim M, Waraich A (2019) Remote health monitoring of elderly through wearable sensors. Multimedia Tools Appl 78(17):24681–24706

    Article  Google Scholar 

  • Appelboom G, Camacho E, Abraham ME, Bruce SS, Dumont EL, Zacharia BE, D’Amico R, Slomian J, Reginster JY, Bruyère O et al (2014) Smart wearable body sensors for patient self-assessment and monitoring. Arch Public Health 72(1):28

    Article  Google Scholar 

  • Avila CO (2019) Novel use of apple watch 4 to obtain 3-lead electrocardiogram and detect cardiac ischemia. Permanente J 23

  • Balaji T, Annavarapu CSR, Bablani A (2021) Machine learning algorithms for social media analysis: a survey. Comput Sci Rev 40:100395

    Article  Google Scholar 

  • Banerjee S, Paul S, Sharma R, Brahma A (2018) Heartbeat monitoring using iot. In: 2018 IEEE 9th annual information technology, electronics and mobile communication conference (IEMCON), IEEE, pp 894–900

  • Bertsimas D, Dunn J, Paschalidis A (2017) Regression and classification using optimal decision trees. In: 2017 IEEE MIT undergraduate research technology conference (URTC), IEEE, pp 1–4

  • Breiman L (2001) Random forests machine learning, vol 45

  • Cadmus-Bertram L, Gangnon R, Wirkus EJ, Thraen-Borowski KM, Gorzelitz-Liebhauser J (2017) Accuracy of heart rate monitoring by some wrist-worn activity trackers. Ann Intern Med 167(8):607–608

    Article  Google Scholar 

  • Dwivedi AK (2018) Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput Appl 29(10):685–693

    Article  Google Scholar 

  • Elumalai A, Maruthi PB, Gautam N, Priyadharshini S, Suganthy M (2021) Optimal prediction of attacks and arterial stiffness effects on heart disease by hybrid machine learning algorithm. J Ambient Intell Hum Comput:1–11

  • Erkan U (2021) A precise and stable machine learning algorithm: eigenvalue classification (eigenclass). Neural Comput Appl 33(10):5381–5392

    Article  Google Scholar 

  • Erkan U, Thanh DN (2019) Autism spectrum disorder detection with machine learning methods. Curr Psychiatry Res Rev Former Curr Psychiatry Rev 15(4):297–308

    Google Scholar 

  • Gropler MR, Dalal AS, Van Hare GF, Silva JNA (2018) Can smartphone wireless ecgs be used to accurately assess ecg intervals in pediatrics? a comparison of mobile health monitoring to standard 12-lead ecg. PloS One 13(9)

  • Hossain ME, Uddin S, Khan A (2021) Network analytics and machine learning for predictive risk modelling of cardiovascular disease in patients with type 2 diabetes. Expert Syst Appl 164:113918

    Article  Google Scholar 

  • Janosi A, William Steinbrunn MP, Detrano R (1998) Heart disease data set -uci repository of machine learning databases. https://archive.ics.uci.edu/ml/datasets/Heart+Disease

  • Jin D, Adams H, Cocco AM, Martin WG, Palmer S (2020) Smartphones and wearable technology: benefits and concerns in cardiology. Med J Aust 212(2):54–56

    Article  Google Scholar 

  • Kasthuri A (2018) Challenges to healthcare in India—the five a’s. Indian J Community Med 43(3):141

    Google Scholar 

  • Kogan S, Zeng Q, Ash N, Greenes RA (2001) Problems and challenges in patient information retrieval: a descriptive study. In: Proceedings of the AMIA symposium, American Medical Informatics Association, p 329

  • Kumar DP, Amgoth T, Annavarapu CSR (2019) Machine learning algorithms for wireless sensor networks: a survey. Inf Fusion 49:1–25

    Article  Google Scholar 

  • Liaw A, Wiener M et al (2002) Classification and regression by randomforest. R news 2(3):18–22

    Google Scholar 

  • Madjarov G, Kocev D, Gjorgjevikj D, Džeroski S (2012) An extensive experimental comparison of methods for multi-label learning. Pattern Recogn 45(9):3084–3104

    Article  Google Scholar 

  • Malasinghe LP, Ramzan N, Dahal K (2019) Remote patient monitoring: a comprehensive study. J Ambient Intell Hum Comput 10(1):57–76

    Article  Google Scholar 

  • Manas M, Sinha A, Sharma S, Mahboob MR (2019) A novel approach for iot based wearable health monitoring and messaging system. J Ambient Intell Hum Comput 10(7):2817–2828

    Article  Google Scholar 

  • Manikandan S (2017) Heart attack prediction system. In: 2017 International conference on energy. communication, data analytics and soft computing (ICECDS), IEEE, pp 817–820

  • Mekuria DN, Sernani P, Falcionelli N, Dragoni AF (2019) Smart home reasoning systems: a systematic literature review. J Ambient Intell Hum Comput:1–18

  • Memiş S, Enginoğlu S, Erkan U (2021) Numerical data classification via distance-based similarity measures of fuzzy parameterized fuzzy soft matrices. IEEE Access 9:88583–88601

    Article  Google Scholar 

  • Meng Y, Speier W, Shufelt C, Joung S, Van Eyk JE, Merz CNB, Lopez M, Spiegel B, Arnold CW (2019) A machine learning approach to classifying self-reported health status in a cohort of patients with heart disease using activity tracker data. IEEE J Biomed Health Inform 24(3):878–884

    Article  Google Scholar 

  • Microsoft, Hospitals A (2018) Introduce ai powered cardiovascular disease risk score api. https://news.microsoft.com/en-in/microsoft-and-apollo-hospitals-introduce-ai-powered-cardiovascular-disease-risk-score/

  • Nandhini SA, Hemalatha R, Radha S, Indumathi K (2018) Web enabled plant disease detection system for agricultural applications using wmsn. Wirel Pers Commun 102(2):725–740

    Article  Google Scholar 

  • Obermeyer Z, Emanuel EJ (2016) Predicting the future-big data, machine learning, and clinical medicine. N Engl J Med 375(13):1216

    Article  Google Scholar 

  • Pascual-Leone A, Fox MD (2018) Identifying individual target sites for transcranial magnetic stimulation applications. US Patent 10,137,307

  • Patel S, Park H, Bonato P, Chan L, Rodgers M (2012) A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil 9(1):21

    Article  Google Scholar 

  • Ramalingam V, Dandapath A, Raja MK (2018) Heart disease prediction using machine learning techniques: a survey. Int J Eng Technol 7(28):684–687

    Article  Google Scholar 

  • Sopic D, Aminifar A, Aminifar A, Atienza D (2018) Real-time event-driven classification technique for early detection and prevention of myocardial infarction on wearable systems. IEEE Trans Biomed Circuits Syst 12(5):982–992

    Article  Google Scholar 

  • Sowmiya C, Sumitra P (2017) Analytical study of heart disease diagnosis using classification techniques. In: 2017 IEEE international conference on intelligent techniques in control optimization and signal processing (INCOS), IEEE, pp 1–5

  • Stewart J, Manmathan G, Wilkinson P (2017) Primary prevention of cardiovascular disease: a review of contemporary guidance and literature. JRSM Cardiovasc Dis 6:2048004016687211

    Google Scholar 

  • Tao R, Zhang S, Huang X, Tao M, Ma J, Ma S, Zhang C, Zhang T, Tang F, Lu J et al (2018) Magnetocardiography-based ischemic heart disease detection and localization using machine learning methods. IEEE Trans Biomed Eng 66(6):1658–1667

    Article  Google Scholar 

  • Thomas J, Princy RT (2016) Human heart disease prediction system using data mining techniques. In: 2016 International conference on circuit. power and computing technologies (ICCPCT), IEEE, pp 1–5

  • Ting KM (2010) Confusion matrix. Encycl Mach Learn 1:209

    Google Scholar 

  • Walker AL, Muhlestein JB (2018) Smartphone electrocardiogram monitoring: current perspectives. Adv Healthc Technol 4:15–24

    Google Scholar 

  • Wan J, Al-awlaqi MA, Li M, O’Grady M, Gu X, Wang J (2018) Cao N (2018) Wearable iot enabled real-time health monitoring system. EURASIP J Wirel Commun Netw 1:298

    Article  Google Scholar 

  • WHO (2016) Cardio vascular disease. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

  • Xie J, Wu R, Wang H, Chen H, Xu X, Kong Y, Zhang W (2021) Prediction of cardiovascular diseases using weight learning based on density information. Neurocomputing

Download references

Author information

Authors and Affiliations

  1. Department of CSE, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    S. V. Jansi Rani, K. R. Sarath Chandran, Akshaya Ranganathan, M. Chandrasekharan, B. Janani & G. Deepsheka

Authors
  1. S. V. Jansi Rani
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. K. R. Sarath Chandran
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Akshaya Ranganathan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. M. Chandrasekharan
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. B. Janani
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. G. Deepsheka
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to S. V. Jansi Rani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jansi Rani, S.V., Chandran, K.R.S., Ranganathan, A. et al. Smart wearable model for predicting heart disease using machine learning. J Ambient Intell Human Comput 13, 4321–4332 (2022). https://doi.org/10.1007/s12652-022-03823-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-022-03823-y