Researchers developed a two-stage machine learning framework that detected diabetes and classified records as prediabetes, type 1, type 2, or type 3c diabetes using two public datasets. XGBoost showed strong internal performance, but inconsistent model rankings, non-standard variables, and the absence of external clinical validation limit immediate use.