Enhanced diabetes prediction using skip-gated recurrent unit with gradient clipping approach

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.