Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features

• URL: http://arxiv.org/abs/2308.01930v1
• Date: Wed, 2 Aug 2023 14:10:03 GMT
• Title: Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features
• Authors: Filipe A. C. Oliveira, Felipe M. Dias, Marcelo A. F. Toledo, Diego A. C. Cardenas, Douglas A. Almeida, Estela Ribeiro, Jose E. Krieger, Marco A. Gutierrez
• Abstract summary: Diabetes is a prevalent chronic condition that compromises the health of millions of people worldwide. Here, we present an alternative method to overcome shortcomings based on non-invasive optical photoplethysmography for detecting diabetes. We classify non-Diabetic and Diabetic patients using the PPG signal and algorithms for training Logistic Regression and eXtreme Gradient Boosting. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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