Meta Learning for Healthcare: Few-Shot Diabetes Prediction Across Heterogeneous Populations

Abstract

Diabetes prediction plays a crucial role in early diagnosis and effective clinical decision-making. Traditional machine learning (ML) models have been widely applied for this task; however, their performance often declines when evaluated on data from different populations. In this study, we present a comparative analysis of classical ML models-Naïve Bayes, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), k-Nearest Neighbors (KNN), and Support Vector Machines (SVM)-alongside an Artificial Neural Network (ANN) and a Few-Shot Meta-Learning approach (Prototypical Network for Tabular Data, Prototypical Networks). The models were trained on the PIMA Indian Diabetes dataset and tested on an external dataset, DiabeticsSylhet, collected from Sylhet Diabetic Hospital, enabling robust cross-population evaluation. Experimental results demonstrate that SVM and ANN both achieved high test accuracy of 94.9 %, while Prototypical Networks outperformed all models with a 96.0 % accuracy, highlighting its superior generalization capability. Compared to conventional ML and ANN approaches, Prototypical Networks showed greater robustness against domain shifts by leveraging episodic training. The novelty of this work lies in applying and evaluating FewShot Meta-Learning for diabetes prediction across heterogeneous datasets, bridging the gap between controlled experimental settings and real-world clinical data. These findings suggest that Meta-Learning frameworks hold strong potential for enhancing the reliability of computer-aided diagnosis in healthcare.

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