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A Predictive Analysis of Heart Rates Using Machine Learning Techniques

Author

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  • Matthew Oyeleye

    (Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Tianhua Chen

    (Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Sofya Titarenko

    (Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Grigoris Antoniou

    (Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

Abstract

Heart disease, caused by low heart rate, is one of the most significant causes of mortality in the world today. Therefore, it is critical to monitor heart health by identifying the deviation in the heart rate very early, which makes it easier to detect and manage the heart’s function irregularities at a very early stage. The fast-growing use of advanced technology such as the Internet of Things (IoT), wearable monitoring systems and artificial intelligence (AI) in the healthcare systems has continued to play a vital role in the analysis of huge amounts of health-based data for early and accurate disease detection and diagnosis for personalized treatment and prognosis evaluation. It is then important to analyze the effectiveness of using data analytics and machine learning to monitor and predict heart rates using wearable device (accelerometer)-generated data. Hence, in this study, we explored a number of powerful data-driven models including the autoregressive integrated moving average (ARIMA) model, linear regression, support vector regression (SVR), k-nearest neighbor (KNN) regressor, decision tree regressor, random forest regressor and long short-term memory (LSTM) recurrent neural network algorithm for the analysis of accelerometer data to make future HR predictions from the accelerometer’s univariant HR time-series data from healthy people. The performances of the models were evaluated under different durations. Evaluated on a very recently created data set, our experimental results demonstrate the effectiveness of using an ARIMA model with a walk-forward validation and linear regression for predicting heart rate under all durations and other models for durations longer than 1 min. The results of this study show that employing these data analytics techniques can be used to predict future HR more accurately using accelerometers.

Suggested Citation

  • Matthew Oyeleye & Tianhua Chen & Sofya Titarenko & Grigoris Antoniou, 2022. "A Predictive Analysis of Heart Rates Using Machine Learning Techniques," IJERPH, MDPI, vol. 19(4), pages 1-14, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2417-:d:753520
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    References listed on IDEAS

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    2. Quang Hung Nguyen & Hai-Bang Ly & Lanh Si Ho & Nadhir Al-Ansari & Hiep Van Le & Van Quan Tran & Indra Prakash & Binh Thai Pham, 2021. "Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, February.
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    Cited by:

    1. Olga Vl. Bitkina & Jaehyun Park & Jungyoon Kim, 2022. "Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods," IJERPH, MDPI, vol. 19(16), pages 1-14, August.
    2. Elisabeth Maria Balint & Peter Angerer & Harald Guendel & Birgitt Marten-Mittag & Marc N. Jarczok, 2022. "Stress Management Intervention for Leaders Increases Nighttime SDANN: Results from a Randomized Controlled Trial," IJERPH, MDPI, vol. 19(7), pages 1-12, March.

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