Kamshetty Chinnababu Suhas, Jayachandra Ananda Babu, Yogesh Swathi Holalu, Abouhawwash Mohamed, Khafaga Doaa Sami, Aldakheel Eman Abdullah, Nagaraju Vinaykumar Vajjanakurike
Department of Information Science and Engineering, Malnad College of Engineering, Hassan, India.
Visvesvaraya Technological University, Belagavi, India.
Front Endocrinol (Lausanne). 2025 Aug 26;16:1601883. doi: 10.3389/fendo.2025.1601883. eCollection 2025.
Diabetes mellitus is a metabolic disorder categorized using hyperglycemia that results from the body's inability to adequately secrete and respond to insulin. Disease prediction using various machine learning (ML) approaches has gained attention because of its potential for early detection. However, it is a challenging task for ML-based algorithms to capture the long-term dependencies like glucose levels in the diabetes data. Hence, this research developed the skip-gated recurrent unit (Skip-GRU) with gradient clipping (GC) approach which is a deep learning (DL)-based approach to predict diabetes effectively. The Skip-GRU network effectively captures the long-term dependencies, and it ignores the unnecessary features and provides only the relevant features for diabetes prediction. The GC technique is used during the training process of the Skip-GRU network that mitigates the exploding gradients issue and helps to predict diabetes effectively. The proposed Skip-GRU with GC approach achieved 98.23% accuracy on a PIMA dataset and 97.65% accuracy on a LMCH dataset. The proposed approach effectively predicts diabetes compared with the existing conventional ML-based approaches.
糖尿病是一种代谢紊乱疾病，其特征为高血糖，这是由于身体无法充分分泌胰岛素并对其做出反应所致。利用各种机器学习（ML）方法进行疾病预测因其早期检测的潜力而受到关注。然而，对于基于ML的算法来说，捕捉糖尿病数据中葡萄糖水平等长期依赖性是一项具有挑战性的任务。因此，本研究开发了带有梯度裁剪（GC）方法的跳跃门控循环单元（Skip-GRU），这是一种基于深度学习（DL）的方法，能够有效预测糖尿病。Skip-GRU网络有效地捕捉长期依赖性，忽略不必要的特征，仅为糖尿病预测提供相关特征。在Skip-GRU网络的训练过程中使用了GC技术，该技术减轻了梯度爆炸问题，并有助于有效预测糖尿病。所提出的带有GC方法的Skip-GRU在PIMA数据集上的准确率达到98.23%，在LMCH数据集上的准确率达到97.65%。与现有的基于传统ML的方法相比，所提出的方法能有效预测糖尿病。
