Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features

  • Filipe A. C. Oliveira USP
  • Felipe M. Dias USP
  • Marcelo A. F. Toledo USP
  • Diego A. C. Cardenas USP
  • Douglas A. Almeida USP
  • Estela Ribeiro USP
  • Jose E. Krieger USP
  • Marco A. Gutierrez USP


Diabetes is a chronic condition which prevention and control is done mostly by minimally invasive devices. In this work, we propose a noninvasive method based on photoplethysmography (PPG) for cost-effective and discomfort-free diabetes detection and prevention. We used PPG signal features and patient metadata from a public dataset for classifying subjects as Diabetic or non-Diabetic. The Logistic Regression and eXtreme Gradient Boosting algorithms were evaluated using a five-fold cross validation approach and achieved a mean AUC of 0.79 ± 0.15 and 0.73 ± 0.17, respectively. Our results align with existing literature, supporting the use of machine learning techniques for developing non-invasive diabetes detection and prevention devices.


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OLIVEIRA, Filipe A. C.; DIAS, Felipe M.; TOLEDO, Marcelo A. F.; CARDENAS, Diego A. C.; ALMEIDA, Douglas A.; RIBEIRO, Estela; KRIEGER, Jose E.; GUTIERREZ, Marco A.. Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 94-105. ISSN 2763-8952. DOI:

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