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Multi-Parametric Glucose Prediction Using Multi-Layer LSTM

Yıl 2023, Sayı: 52, 169 - 175, 15.12.2023

Ömer Atılım Koca Volkan Kılıç

Öz

Diabetes causes irregular glucose levels, such as hyperglycemia (high glucose) and hypoglycemia (low glucose), which affect the quality of life of diabetes patients. Early detection of hyperglycemia and hypoglycemia is important for effective management of the disease. In recent years, progress has been made in the development of artificial intelligence-based tools for effective diabetes management. These tools aim to predict glucose levels before they reach critical levels, enabling people with diabetes to take proactive measures to keep their glucose levels within a healthy range. However, most of these tools use single-layer architectures and rely only on glucose measurement as a predictive parameter, thus resulting in low predictive accuracy. Here, this paper proposes a multi-layer Long-Short Term Memory (LSTM)-based model for glucose prediction. The proposed model was tested on the OhioT1DM dataset and the lowest Root Mean Square Error value was obtained as 14.364 mg/dL for glucose prediction over a 30-min prediction horizon. The results demonstrate the performance of the proposed system, which uses a multi-layer LSTM algorithm to overcome the complex memory operations associated with multi-parameter prediction.

Anahtar Kelimeler

Artificial Intelligence LSTM Diabetes Glucose Prediction

Proje Numarası

222S488 ve 2023-TYL-FEBE-0025

Teşekkür

This research was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) (No. 222S488) and by the scientific research projects coordination unit of Izmir Katip Celebi University (No: 2023-TYL-FEBE-0025).

Kaynakça

  • Akosman, Ş. A., Öktem, M., Moral, Ö. T., & Kılıç, V. (2021). Deep Learning-based Semantic Segmentation for Crack Detection on Marbles. 29th Signal Processing and Communications Applications Conference (SIU),
  • Aliberti, A., Pupillo, I., Terna, S., Macii, E., Di Cataldo, S., Patti, E., & Acquaviva, A. (2019). A multi-patient data-driven approach to blood glucose prediction. IEEE Access, 7, 69311-69325.
  • Aydın, S., Çaylı, Ö., Kılıç, V., & Onan, A. (2022). Sequence-to-sequence video captioning with residual connected gated recurrent units. Avrupa Bilim ve Teknoloji Dergisi(35), 380-386.
  • Betül, U., Çaylı, Ö., Kılıç, V., & Onan, A. (2022). Resnet based deep gated recurrent unit for image captioning on smartphone. Avrupa Bilim ve Teknoloji Dergisi(35), 610-615.
  • Bhimireddy, A., Sinha, P., Oluwalade, B., Gichoya, J. W., & Purkayastha, S. (2020). Blood glucose level prediction as time-series modeling using sequence-to-sequence neural networks.
  • Chen, G. (2016). A gentle tutorial of recurrent neural network with error backpropagation. arXiv preprint arXiv:1610.02583.
  • Çaylı, Ö., Kılıç, V., Onan, A., & Wang, W. (2022). Auxiliary classifier based residual rnn for image captioning. 30th European Signal Processing Conference (EUSIPCO),
  • Çaylı, Ö., Liu, X., Kılıç, V., & Wang, W. (2023). Knowledge Distillation for Efficient Audio-Visual Video Captioning. arXiv preprint arXiv:2306.09947.
  • Çaylı, Ö., Makav, B., Kılıç, V., & Onan, A. (2021). Mobile application based automatic caption generation for visually impaired. Intelligent and Fuzzy Techniques: Smart and Innovative Solutions: Proceedings of the INFUS 2020 Conference, Istanbul, Turkey,
  • Doǧan, V., Isık, T., Kılıç, V., & Horzum, N. (2022). A field-deployable water quality monitoring with machine learning-based smartphone colorimetry. Analytical Methods 14(35), 3458-3466.
  • Fetiler, B., Çaylı, Ö., Moral, Ö. T., Kılıç, V., & Onan, A. (2021). Video captioning based on multi-layer gated recurrent unit for smartphones. Avrupa Bilim ve Teknoloji Dergisi(32), 221-226.
  • Graves, A., Mohamed, A.-r., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. IEEE international conference on acoustics, speech and signal processing,
  • Keskin, R., Çaylı, Ö., Moral, Ö. T., Kılıç, V., & Onan, A. (2021). A benchmark for feature-injection architectures in image captioning. Avrupa Bilim ve Teknoloji Dergisi(31), 461-468.
  • Kılıç, V. (2021). Yapay Zeka Tabanlı Akıllı Telefon Uygulaması ile Kan Şekeri Tahmini. Avrupa Bilim ve Teknoloji Dergisi(26), 289-294.
  • Kılıç, V., Mercan, Ö. B., Tetik, M., Kap, Ö., & Horzum, N. (2022). Non-enzymatic colorimetric glucose detection based on Au/Ag nanoparticles using smartphone and machine learning. Analytical Sciences, 38(2), 347-358.
  • Li, K., Daniels, J., Liu, C., Herrero, P., & Georgiou, P. (2019). Convolutional recurrent neural networks for glucose prediction. IEEE journal of biomedical and health informatics, 24(2), 603-613.
  • Li, K., Liu, C., Zhu, T., Herrero, P., & Georgiou, P. (2019). GluNet: A deep learning framework for accurate glucose forecasting. IEEE journal of biomedical and health informatics, 24(2), 414-423.
  • Marling, C., & Bunescu, R. (2020). The OhioT1DM dataset for blood glucose level prediction. CEUR workshop proceedings,
  • Mercan, Ö. B., Doğan, V., & Kılıç, V. (2020). Time Series Analysis based Machine Learning Classification for Blood Sugar Levels. 2020 Medical Technologies Congress (TIPTEKNO),
  • Mercan, Ö. B., & Kılıç, V. (2020). Deep learning based colorimetric classification of glucose with au-ag nanoparticles using smartphone. 2020 Medical Technologies Congress (TIPTEKNO),
  • Palaz, Z., Doğan, V., & Kılıç, V. (2021). Smartphone-based Multi-parametric Glucose Prediction using Recurrent Neural Networks. Avrupa Bilim ve Teknoloji Dergisi(32), 1168-1174.
  • Sayraci, B., Ağralı, M., & Kılıç, V. (2023). Artificial Intelligence Based Instance-Aware Semantic Lobe Segmentation on Chest Computed Tomography Images. Avrupa Bilim ve Teknoloji Dergisi(46), 109-115.
  • Song, W., Cai, W., Li, J., Jiang, F., & He, S. (2019). Predicting blood glucose levels with EMD and LSTM based CGM data. 6th International Conference on Systems and Informatics (ICSAI),
  • Strollo, F., Furia, A., Verde, P., Bellia, A., Grussu, M., Mambro, A., Petrelli, M., & Gentile, S. (2021). Technological innovation of Continuous Glucose Monitoring (CGM) as a tool for commercial aviation pilots with insulin-treated diabetes and stakeholders/regulators: A new chance to improve the directives? diabetes research and clinical practice, 172, 108638.
  • Şen, M., Yüzer, E., Doğan, V., Avcı, İ., Ensarioğlu, K., Aykaç, A., Kaya, N., Can, M., & Kılıç, V. (2022). Colorimetric detection of H2O2 with Fe3O4@ Chi nanozyme modified µPADs using artificial intelligence. Microchimica Acta, 189(10), 373.
  • Wang, W., Tong, M., & Yu, M. (2020). Blood glucose prediction with VMD and LSTM optimized by improved particle swarm optimization. IEEE Access, 8, 217908-217916.
  • Zhang, M., Flores, K. B., & Tran, H. T. (2021). Deep learning and regression approaches to forecasting blood glucose levels for type 1 diabetes. Biomedical Signal Processing and Control, 69, 102923.
  • Zhu, T., Li, K., Chen, J., Herrero, P., & Georgiou, P. (2020). Dilated recurrent neural networks for glucose forecasting in type 1 diabetes. Journal of Healthcare Informatics Research, 4, 308-324.

Çok Katmanlı LSTM Kullanarak Çok Parametreli Glikoz Tahmini

Yıl 2023, Sayı: 52, 169 - 175, 15.12.2023

Ömer Atılım Koca Volkan Kılıç

Öz

Diyabet, hastaların yaşam kalitesini etkileyen hiperglisemi (yüksek glikoz) ve hipoglisemi (düşük glikoz) gibi düzensiz glikoz seviyelerine neden olmaktadır. Hiperglisemi ve hipogliseminin erken teşhisi bu hastalığın etkin yönetimi için önemlidir. Son yıllarda, etkili diyabet yönetimi için yapay zeka tabanlı araçların geliştirilmesinde ilerleme kaydedilmiştir. Bu araçlar, glikoz seviyelerini kritik seviyelere ulaşmadan önce tahmin etmeyi ve diyabetli kişilerin glikoz seviyelerini sağlıklı bir aralıkta tutmak için proaktif önlemler almalarını sağlamayı amaçlamaktadır. Ancak, bu araçların çoğu tek katmanlı mimariler kullanmakta ve tahmin parametresi olarak yalnızca glikoz ölçümüne dayanmakta, dolayısıyla düşük tahmin doğruluğu ile sonuçlanmaktadır. Bu makalede, glikoz tahmini için çok katmanlı Uzun-Kısa Süreli Bellek (LSTM) tabanlı bir model önerilmektedir. Önerilen model OhioT1DM veri kümesi üzerinde test edilmiş ve 30 dakikalık bir tahmin ufku boyunca glikoz tahmini için en düşük Kök Ortalama Kare Hata değeri 14.364 mg/dL olarak elde edilmiştir. Sonuçlar, çok parametreli tahminle ilişkili karmaşık bellek işlemlerinin üstesinden gelmek için çok katmanlı bir LSTM algoritması kullanan önerilen sistemin performansını göstermektedir.

Anahtar Kelimeler

Yapay Zeka LSTM Diyabet Glikoz Tahmini

Proje Numarası

222S488 ve 2023-TYL-FEBE-0025

Kaynakça

  • Akosman, Ş. A., Öktem, M., Moral, Ö. T., & Kılıç, V. (2021). Deep Learning-based Semantic Segmentation for Crack Detection on Marbles. 29th Signal Processing and Communications Applications Conference (SIU),
  • Aliberti, A., Pupillo, I., Terna, S., Macii, E., Di Cataldo, S., Patti, E., & Acquaviva, A. (2019). A multi-patient data-driven approach to blood glucose prediction. IEEE Access, 7, 69311-69325.
  • Aydın, S., Çaylı, Ö., Kılıç, V., & Onan, A. (2022). Sequence-to-sequence video captioning with residual connected gated recurrent units. Avrupa Bilim ve Teknoloji Dergisi(35), 380-386.
  • Betül, U., Çaylı, Ö., Kılıç, V., & Onan, A. (2022). Resnet based deep gated recurrent unit for image captioning on smartphone. Avrupa Bilim ve Teknoloji Dergisi(35), 610-615.
  • Bhimireddy, A., Sinha, P., Oluwalade, B., Gichoya, J. W., & Purkayastha, S. (2020). Blood glucose level prediction as time-series modeling using sequence-to-sequence neural networks.
  • Chen, G. (2016). A gentle tutorial of recurrent neural network with error backpropagation. arXiv preprint arXiv:1610.02583.
  • Çaylı, Ö., Kılıç, V., Onan, A., & Wang, W. (2022). Auxiliary classifier based residual rnn for image captioning. 30th European Signal Processing Conference (EUSIPCO),
  • Çaylı, Ö., Liu, X., Kılıç, V., & Wang, W. (2023). Knowledge Distillation for Efficient Audio-Visual Video Captioning. arXiv preprint arXiv:2306.09947.
  • Çaylı, Ö., Makav, B., Kılıç, V., & Onan, A. (2021). Mobile application based automatic caption generation for visually impaired. Intelligent and Fuzzy Techniques: Smart and Innovative Solutions: Proceedings of the INFUS 2020 Conference, Istanbul, Turkey,
  • Doǧan, V., Isık, T., Kılıç, V., & Horzum, N. (2022). A field-deployable water quality monitoring with machine learning-based smartphone colorimetry. Analytical Methods 14(35), 3458-3466.
  • Fetiler, B., Çaylı, Ö., Moral, Ö. T., Kılıç, V., & Onan, A. (2021). Video captioning based on multi-layer gated recurrent unit for smartphones. Avrupa Bilim ve Teknoloji Dergisi(32), 221-226.
  • Graves, A., Mohamed, A.-r., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. IEEE international conference on acoustics, speech and signal processing,
  • Keskin, R., Çaylı, Ö., Moral, Ö. T., Kılıç, V., & Onan, A. (2021). A benchmark for feature-injection architectures in image captioning. Avrupa Bilim ve Teknoloji Dergisi(31), 461-468.
  • Kılıç, V. (2021). Yapay Zeka Tabanlı Akıllı Telefon Uygulaması ile Kan Şekeri Tahmini. Avrupa Bilim ve Teknoloji Dergisi(26), 289-294.
  • Kılıç, V., Mercan, Ö. B., Tetik, M., Kap, Ö., & Horzum, N. (2022). Non-enzymatic colorimetric glucose detection based on Au/Ag nanoparticles using smartphone and machine learning. Analytical Sciences, 38(2), 347-358.
  • Li, K., Daniels, J., Liu, C., Herrero, P., & Georgiou, P. (2019). Convolutional recurrent neural networks for glucose prediction. IEEE journal of biomedical and health informatics, 24(2), 603-613.
  • Li, K., Liu, C., Zhu, T., Herrero, P., & Georgiou, P. (2019). GluNet: A deep learning framework for accurate glucose forecasting. IEEE journal of biomedical and health informatics, 24(2), 414-423.
  • Marling, C., & Bunescu, R. (2020). The OhioT1DM dataset for blood glucose level prediction. CEUR workshop proceedings,
  • Mercan, Ö. B., Doğan, V., & Kılıç, V. (2020). Time Series Analysis based Machine Learning Classification for Blood Sugar Levels. 2020 Medical Technologies Congress (TIPTEKNO),
  • Mercan, Ö. B., & Kılıç, V. (2020). Deep learning based colorimetric classification of glucose with au-ag nanoparticles using smartphone. 2020 Medical Technologies Congress (TIPTEKNO),
  • Palaz, Z., Doğan, V., & Kılıç, V. (2021). Smartphone-based Multi-parametric Glucose Prediction using Recurrent Neural Networks. Avrupa Bilim ve Teknoloji Dergisi(32), 1168-1174.
  • Sayraci, B., Ağralı, M., & Kılıç, V. (2023). Artificial Intelligence Based Instance-Aware Semantic Lobe Segmentation on Chest Computed Tomography Images. Avrupa Bilim ve Teknoloji Dergisi(46), 109-115.
  • Song, W., Cai, W., Li, J., Jiang, F., & He, S. (2019). Predicting blood glucose levels with EMD and LSTM based CGM data. 6th International Conference on Systems and Informatics (ICSAI),
  • Strollo, F., Furia, A., Verde, P., Bellia, A., Grussu, M., Mambro, A., Petrelli, M., & Gentile, S. (2021). Technological innovation of Continuous Glucose Monitoring (CGM) as a tool for commercial aviation pilots with insulin-treated diabetes and stakeholders/regulators: A new chance to improve the directives? diabetes research and clinical practice, 172, 108638.
  • Şen, M., Yüzer, E., Doğan, V., Avcı, İ., Ensarioğlu, K., Aykaç, A., Kaya, N., Can, M., & Kılıç, V. (2022). Colorimetric detection of H2O2 with Fe3O4@ Chi nanozyme modified µPADs using artificial intelligence. Microchimica Acta, 189(10), 373.
  • Wang, W., Tong, M., & Yu, M. (2020). Blood glucose prediction with VMD and LSTM optimized by improved particle swarm optimization. IEEE Access, 8, 217908-217916.
  • Zhang, M., Flores, K. B., & Tran, H. T. (2021). Deep learning and regression approaches to forecasting blood glucose levels for type 1 diabetes. Biomedical Signal Processing and Control, 69, 102923.
  • Zhu, T., Li, K., Chen, J., Herrero, P., & Georgiou, P. (2020). Dilated recurrent neural networks for glucose forecasting in type 1 diabetes. Journal of Healthcare Informatics Research, 4, 308-324.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka (Diğer)
Bölüm Makaleler
Yazarlar

Ömer Atılım Koca 0009-0007-7286-6785

Volkan Kılıç 0000-0002-3164-1981

Proje Numarası 222S488 ve 2023-TYL-FEBE-0025
Erken Görünüm Tarihi 5 Aralık 2023
Yayımlanma Tarihi 15 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Sayı: 52

Kaynak Göster

APA Koca, Ö. A., & Kılıç, V. (2023). Multi-Parametric Glucose Prediction Using Multi-Layer LSTM. Avrupa Bilim Ve Teknoloji Dergisi(52), 169-175.
 Kapak Resmi İndir