  Diabetes is a prevalent chronic condition that compromises the health of
millions of people worldwide. Minimally invasive methods are needed to prevent
and control diabetes but most devices for measuring glucose levels are invasive
and not amenable for continuous monitoring. Here, we present an alternative
method to overcome these shortcomings based on non-invasive optical
photoplethysmography (PPG) for detecting diabetes. We classify non-Diabetic and
Diabetic patients using the PPG signal and metadata for training Logistic
Regression (LR) and eXtreme Gradient Boosting (XGBoost) algorithms. We used PPG
signals from a publicly available dataset. To prevent overfitting, we divided
the data into five folds for cross-validation. By ensuring that patients in the
training set are not in the testing set, the model's performance can be
evaluated on unseen subjects' data, providing a more accurate assessment of its
generalization. Our model achieved an F1-Score and AUC of $58.8\pm20.0\%$ and
$79.2\pm15.0\%$ for LR and $51.7\pm16.5\%$ and $73.6\pm17.0\%$ for XGBoost,
respectively. Feature analysis suggested that PPG morphological features
contains diabetes-related information alongside metadata. Our findings are
within the same range reported in the literature, indicating that machine
learning methods are promising for developing remote, non-invasive, and
continuous measurement devices for detecting and preventing diabetes.
