Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features
This work addresses the need for minimally invasive monitoring methods for diabetes patients, but it is incremental as it applies existing machine learning techniques to a known dataset with results comparable to prior literature.
The paper tackled the problem of non-invasive diabetes detection by using photoplethysmography (PPG) signals and metadata with machine learning models like Logistic Regression and XGBoost, achieving an F1-Score of 58.8% and AUC of 79.2% for LR, and 51.7% and 73.6% for XGBoost, respectively.
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.