Prediction of maturity-onset diabetes of the young subtypes using machine learning

Maturity-onset diabetes of the young (MODY) is a monogenic type of diabetes caused by different pathogenic genetic variants in glucose metabolism-related genes, with GCK-MODY and HFN1A-MODY subtypes being the most frequent. Diagnosing the specific MODY subtype is essential for correct treatment and follow-up, but it requires gene sequencing, a time-consuming and costly process that depends on highly skilled professionals. Therefore, it is mandatory to develop tools that allow to correctly determine in which order to study the involved genes, reducing the number of sequencing procedures to find the causal variant and making the diagnostic process more efficient. This proof-of-concept study evaluates machine learning as a complement to clinical characterization and genetic testing, by optimizing binary classification models for explainable prediction of MODY subtypes, with a focus on GCK-MODY and HFN1A-MODY.

To meet this aim, we analyzed medical data from a diabetes cohort from Buenos Aires, Argentina. By employing imputation and oversampling techniques we created 10 datasets for each subtype to feed a pipeline that trained, optimized and evaluated 10 machine learning techniques.

Gaussian Naive Bayes achieved the best predictive power for GCK-MODY with a ROC AUC score of 0.724, meanwhile Random Forest yielded 0.712 for HNF1A-MODY. SHAP analysis provided insights into feature importance, highlighting the explainability of our approach.

This novel study demonstrates for the first time the viability of machine learning as a supplementary tool prior to MODY genetic testing, by providing cost-effective and explainable models able to assist health professionals in the diagnosis of MODY subtypes.