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Early Stage Diabetes Risk Prediction via Machine Learning

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Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (SoCPaR 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 417))

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Abstract

Diabetes disease is triggering many health problems including microvascular diseases, macrovascular abnormalities, and neuropathic diseases. From an economic perspective, diabetes is one of the costliest diseases, moreover, high percentage of adults with diabetes live in low- and middle-income countries triggering more economic troubles to these countries. Diagnosing the risk of diabetes will help combat against this silent killer. In this paper, we propose an inclusive machine learning based predictive model for diagnosing the risk of having diabetes using a recent dataset of signs and symptoms, known as Diabetes Risk Prediction (DRP2020). We employ more than twenty ML techniques on DRP2020 and we evaluate all ML based models using different performance evaluation metrics including accuracy, precision, recall, harmonic mean, prediction speed and alarm errors. We provide extensive simulation results and compare the performance of various ML based models. Accordingly, the model based shallow neural networks (SNN) has been elected as the optimum model for constructing of the early stage diabetes risk prediction scoring a 99.23% and 99.38% for prediction accuracy and the harmonic mean of precision and recall, respectively. The obtained results exhibit the proficiency and distinction of our model over other state-of art models. Eventually, the proposed system can be proficiently deployed as a clinical tool to assist in provide early stage diabetes risk prediction.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Princess Sumaya University of Technology, Amman, 1196, Jordan

    Qasem Abu Al-Haija

  2. Department of Electrical Engineering, The Hashemite University, Zarqa, 13133, Jordan

    Mahmoud Smadi

  3. Department of Biomedical Engineering, The Hashemite University, Zarqa, 13133, Jordan

    Osama M. Al-Bataineh

Authors
  1. Qasem Abu Al-Haija
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  2. Mahmoud Smadi
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  3. Osama M. Al-Bataineh
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Corresponding author

Correspondence to Qasem Abu Al-Haija .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Industrial Engineering and Computer Science, Stellenbosch University, Matieland, South Africa

    Andries Engelbrecht

  3. Department of Computer Science, Università degli Studi di Milano, Milan, Italy

    Fabio Scotti

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. University of Mumbai, Mumbai, Maharashtra, India

    Pooja Manghirmalani Mishra

  6. University of Calabria (Unical), Rende, Italy

    Giancarlo Fortino

  7. Department of Informatics, Vilnius University, Kaunas, Lithuania

    Virgilijus Sakalauskas

  8. Center for Smart Computing Continuum, Forschung Burgenland, Eisenstadt, Austria

    Sabri Pllana

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Al-Haija, Q.A., Smadi, M., Al-Bataineh, O.M. (2022). Early Stage Diabetes Risk Prediction via Machine Learning. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_42

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