Ensemble Learning for Diabetes Prediction: An Integration of TabNet and Neural Oblivious Decision Ensembles (NODE)
Received: 28 September 2025 | Revised: 16 October 2025 and 27 October 2025 | Accepted: 29 October 2025 | Online: 8 December 2025
Corresponding author: Majid Rahardi
Abstract
The accurate prediction of diabetes risk is paramount for advancing healthcare and personalized medicine. This study presents a comparative analysis of advanced deep learning models for structured data, focusing on two novel architectures, Neural Oblivious Decision Ensembles (NODE) and TabNet. The method encompasses comprehensive data preprocessing, including a critical technique to address the imbalanced nature of the dataset (oversampling). Finally, a combined modeling approach (a soft-voting ensemble) was implemented to combine the predictive probabilities from the trained individual models. The soft-voting ensemble demonstrated strong performance, achieving a validation accuracy of 93.55, a precision of 92.60, a recall of 94.58, and an F1-score of 93.58. These findings underscore the potential of advanced deep learning techniques, especially when combined in an ensemble, to provide highly reliable and accurate diabetes risk prediction from complex tabular data.
Keywords:
ensemble learning, deep learning, diabetes prediction, TabNet, NODEDownloads
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Copyright (c) 2025 Majid Rahardi, Ferian Fauzi Abdulloh, Ahlihi Masruro, Bima Pramudya Asaddulloh, Afrig Aminuddin, Nafiatun Sholihah
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