Scholarlink

<p>The aim of this study is to develop an artificial intelligence (AI)-driven pipeline for forecasting blood glucose levels to mitigate risks associated with hypoglycemia and hyperglycemia. The main research question focuses on the effectiveness of hybrid data preprocessing and feature engineering in enhancing glucose level predictions. The proposed approach employs a hybrid methodology for handling missing data and advanced feature engineering techniques. A recurrent neural network (RNN) model is developed to forecast glucose levels with a lead time of 30 minutes. The model is evaluated using metrics such as root mean square error (RMSE) and mean absolute error (MAE). Experimental results indicate that the proposed pipeline achieves an average RMSE of 19.64 ± 0.11 and an MAE of 13.54 ± 0.11 across all patients. The results demonstrate improved forecasting accuracy, enabling early detection of critical glucose fluctuations. The integration of hybrid preprocessing and RNN modeling effectively predicts glucose levels, providing valuable insights for diabetes management. This approach supports better prevention of glucose emergencies, ultimately enhancing the quality of life for individuals with diabetes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhanced glucose forecasting using recurrent neural network and advanced feature engineering

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月9日

查看全文
  • Mahmoud H. Osman,
  • Mennatullah Mahmoud,
  • Salma Zakzouk,
  • Samah Mohamed,
  • Ibrahim Gomaa,
  • M. Saeed Darweesh,
  • Sayed Taha,
  • Ahmed Soltan

摘要

The aim of this study is to develop an artificial intelligence (AI)-driven pipeline for forecasting blood glucose levels to mitigate risks associated with hypoglycemia and hyperglycemia. The main research question focuses on the effectiveness of hybrid data preprocessing and feature engineering in enhancing glucose level predictions. The proposed approach employs a hybrid methodology for handling missing data and advanced feature engineering techniques. A recurrent neural network (RNN) model is developed to forecast glucose levels with a lead time of 30 minutes. The model is evaluated using metrics such as root mean square error (RMSE) and mean absolute error (MAE). Experimental results indicate that the proposed pipeline achieves an average RMSE of 19.64 ± 0.11 and an MAE of 13.54 ± 0.11 across all patients. The results demonstrate improved forecasting accuracy, enabling early detection of critical glucose fluctuations. The integration of hybrid preprocessing and RNN modeling effectively predicts glucose levels, providing valuable insights for diabetes management. This approach supports better prevention of glucose emergencies, ultimately enhancing the quality of life for individuals with diabetes.