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پیش‌بینی میزان قند خون در بیماران مبتلا به دیابت نوع یک بر اساس یادگیری عمیق

نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 کارشناسی ارشد، گروه مهندسی کامپیوتر، دانشکده‌ی مهندسی، دانشگاه گیلان، رشت، ایران

2 کارشناسی، گروه مهندسی کامپیوتر ، دانشکده‌ی مهندسی، دانشگاه گیلان، رشت، ایران

3 دانشیار، گروه مهندسی کامپیوتر، دانشکده‌ی مهندسی، دانشگاه گیلان، رشت، ایران

10.22041/ijbme.2020.134866.1617

چکیده

دیابت یک بیماری شایع در سراسر جهان است. این بیماری، سخت، غیر‌قابل علاج و در عین حال قابل کنترل بوده و از این رو کنترل و پیش‌گیری از عوارض آن امری مهم است. به همین دلیل استفاده از روش‌های هوشمند با خطای پایین برای پیش‌بینی میزان قند خون و از همه مهم‌تر جلوگیری از عوارض خطرناک آن یک مساله‌ی مهم در کنترل بهتر این بیماری است. با توجه به روش‌های مختلف ارائه‌ شده در این زمینه، در این مقاله نیز دو مدل با استفاده از ره‌یافت یادگیری عمیق ارائه شده که نتایج آن کارآمد و بهینه است. این دو مدل‌‌ پیشنهادی از ترکیب‌های متفاوتی از شبکه‌های عصبی حافظه‌ی طولانی کوتاه‌مدت و پیش‌خور تشکیل ‌شده و میزان قند خون آتی بیمار را با دقت و سرعت قابل توجهی پیش‌بینی می‌کنند. در این راستا از 81.200 داده‌ی میزان قند خون 203 بیمار به همراه 27 مشخصه‌ی موثر بر میزان قند خون استفاده شده است. هم‌چنین به منظور ارزیابی دقیق از روش اعتبارسنجی متقابل مناسب برای سری زمانی استفاده شده و نتایج حاصل از اجرای مدل‌ها نشان داده که مدل میانگین متحرک خودهمبسته‌ی یک‌پارچه با توجه به این حجم از داده و ضعف سخت‌افزاری سیستم پیاده‌سازی شده قادر به پیش‌بینی میزان قند خون نبوده در حالی که کارایی و سرعت عمل‌کرد مدل‌های مبتنی بر یادگیری عمیق قابل قبول است. هم‌چنین با توجه به نتایج به دست آمده مدل‌ پیشنهادی دوم برای افق‌های پیش‌بینی 5، 10 و 15 دقیقه به ترتیب 8/13%، 16% و 9/18% بهتر از مدل پیشنهادی اول عمل کرده و مدل قابل اعتمادتری برای پیش‌بینی میزان قند خون است. از این رو مدل‌ پیشنهادی دوم می‌تواند در سیستم‌های هوشمند هشداردهنده برای پیش‌گیری از وقوع هیپوگلیسمی که از عوارض خطرناک و شایع بیماری دیابت نوع یک است مورد استفاده قرار گیرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Blood Glucose Level Prediction for Type 1 Diabetes using Deep Learning

نویسندگان [English]

  • Seyedeh Sadaf Razavinezhad 1
  • Amir mohammad Fallah 2
  • Seyed Abolghasem Mirroshandel 3

1 M.Sc., Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran

2 B.Sc., Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran

3 Associate Professor, Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran

چکیده [English]

Diabetes is a common disease all around the world. It is a difficult and incurable but controllable disease, so it is important to control and prevent its complications. Thus, low error and smart methods are used to predict blood glucose levels and prevent dangerous complications to control it effectively. In this regard, different methods were investigated. In this research, two models based on deep learning technique are used which produce efficient and optimal results. These models are composed of different combinations of long short-term memory and feed forward neural networks which predict the patient's future blood glucose levels with considerable accuracy and speed. To achieve more comprehensive model, 81,200 blood glucose data was evaluated through 203 patients. In addition, 27 effective features in patients' blood glucose levels are considered in this regard. Furthermore, cross-validation method which is suitable for time series was used for more accurate evaluation. The results showed that Autoregressive Integrated Moving Average model could not predict blood glucose levels considering this amount of data and system hardware limitations, while the models based on deep learning had good performance and good speed. Furthermore, the second proposed model for the prediction horizons of 5, 10, and 15 minutes outperformed the first one with 13.8%, 16%, and 18.9%, respectively. Therefore, the second proposed model can be more reliable for predicting blood glucose. So, it can be used in smart warning systems to prevent hypoglycemia, which is a dangerous and widespread problem of type 1 diabetes.

کلیدواژه‌ها [English]

  • blood glucose
  • Type 1 Diabetes
  • prediction
  • Artificial neural network
  • Deep Learning
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فصل نامه علمی پژوهشی مهندسی پزشکی زیستی
دوره 14، شماره 4
بهمن 1399
صفحه 307-320
فایل ها
سابقه مقاله
  • تاریخ دریافت: 24 مرداد 1399
  • تاریخ بازنگری: 24 مهر 1399
  • تاریخ پذیرش: 18 آبان 1399
  • تاریخ اولین انتشار: 18 آبان 1399
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