Developing a Simple Non–Laboratory-Based Machine Learning Tool for Prediabetes Screening in a Target Population: A Proof-of-Concept Study

Background: Progression from prediabetes to type 2 diabetes (T2D) can be delayed with early detection and intervention. Current detection methods, relying on costly blood glucose tests, limit widespread screening. Machine learning models offer the potential for non–laboratory-based tools. However, existing prediabetes detection models lack validation in their intended target populations. Thus, this study aimed to develop and validate a non–laboratory-based machine learning tool for prediabetes detection in a specific target population. Methods: Based on 501 adults from a prediabetes screening project, a decision tree model was developed. Twelve potential non–laboratory-based features were extracted. The target variable was categorized into prediabetes (hemoglobin A1c HbA 1c ≥39 mmol/mol and <48 mmol/mol) and normoglycemia (HbA 1c <39 mmol/mol). The data set was divided into 70% for training and 30% for validation, and forward feature selection was used to identify the most relevant features. Results: Out of 501 participants, 88 were identified with prediabetes. The mean age and body mass index (BMI) were approximately 50 years and 27 in both the training and validation sets. Forward selection identified age and waist circumference as the most important features to include in the model. The model achieved an area under the receiver operating characteristic curve (ROC AUC) of 0.8297 and 0.7961 on the training and validation sets. Conclusion: A machine learning screening tool using age and waist circumference was developed with promising results. Its simplicity, by only requiring two non-laboratory features, allows for easy implementation. However, to verify the model’s generalizability and external validity, it needs to be evaluated using additional data.