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Volume 11, Number 1, January 2026 , pages 1-16 DOI: https://doi.org/10.12989/acd.2026.11.1.001 |
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Implementing LSTM-RNN for improved diabetic dataset classification |
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Wasim Raja, Chandraprabha K., V. Ruckmani and S. Gowri
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| Abstract | ||
| Diabetes is a chronic illness with high morbidity and mortality that influences the quality of life considerably around the globe and thus early and correct prediction is crucial in the effective management and treatment. Nonetheless, the clinical information used is problematic because it is difficult to predict diabetes development in patients, given the complexity and variability of the data. This paper proposes a deep learning-based model with a Long Short-Term Memory (LSTM) recurrent neural network and improved preprocessing and feature selection algorithms. First, Z-score normalization is used to standardize the data, enhancing consistency and identifying abnormalities. Then, to achieve the best feature selection, the Grey Wolf Optimization (GWO) is used to improve predictive performance by identifying the most relevant clinical attributes without falling into local optima. Lastly, the LSTM-RNN model is applied to extract temporal dependencies and latent patterns in the data to correctly classify the data. Through experimentation, it has been shown that the proposed approach clearly exceeds conventional techniques based on all available measures of performance: accuracy; precision; recall; F1 score; and computational efficiency. As indicated by these results, this LSTM-RNN-GWO model shows promise as a valuable resource in the area of predictive analytics related to diabetes care, providing great benefit to patients through its use in early identification of diabetes and subsequent enhancement of their clinical experience. | ||
| Key Words | ||
| early detection; diabetes condition; GWO; LSTM-RNN; mendeley data; standard deviation; z-score normalization | ||
| Address | ||
| Wasim Raja: Department of Computer Science, Jamal Mohamed College (Autonomous), (Affiliated to Bharathidasan University), Tiruchirappalli, Tamilnadu, India Chandraprabha K.: Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, Erode, Tamilnadu, India V. Ruckmani: Department of Computer Science and Applications, KMG College of Arts and Science, Vellore, Tamil Nadu, India S. Gowri: Department of Computer Applications, Dhanalakshmi Srinivasan College of Arts and Science for Women, Perambalur, Tamil Nadu, India | ||
