Enhanced Diabetes Prediction Using Skip-Gated Recurrent Unit with Gradient Clipping Approach

Suhas Kamshetty Chinnababu¹Ananda Babu Jayachandra²Swathi Holalu Yogesh²Mohamed Abouhawwash³Doaa Sami Khafaga⁴Eman Abdullah Aldakheel^4*Vinaykumar Vajjanakurike²

• ¹Channabasaveshwara Institute of Technology, Tumkur, Karnataka, India
• ²Visvesvaraya Technological University, Belgaum, Karnataka, India
• ³King Fahd University of Petroleum and Minerals, Dhahran, Ash Sharqiyah, Saudi Arabia
• ⁴Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Diabetes mellitus is metabolic disorder categorized using hyperglycemia that results from a inadequate of body for secreting and responding 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 Skip-GRU network that mitigate the exploding gradients issue and help 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 while comparing with existing conventional ML-based approaches.