Publikasi Dosen

Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal

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Monitoring systems for the early detection of diabetes are essential to avoid potential
expensive medical costs. Currently, only invasive monitoring methods are commercially available.
These methods have significant disadvantages as patients experience discomfort while obtaining
blood samples. A non-invasive method of blood glucose level (BGL) monitoring that is painless
and low-cost would address the limitations of invasive techniques. Photoplethysmography (PPG)
collects a signal from a finger sensor using a photodiode, and a nearby infrared LED light. The
combination of the PPG electronic circuit with artificial intelligence makes it possible to implement
the classification of BGL. However, one major constraint of deep learning is the long training phase.
We try to overcome this limitation and offer a concept for classifying type 2 diabetes (T2D) using a
machine learning algorithm based on PPG. We gathered 400 raw datasets of BGL measured with PPG
and divided these points into two classification levels, according to the National Institute for Clinical
Excellence, namely, “normal” and “diabetes”. Based on the results for testing between the models,
the ensemble bagged trees algorithm achieved the best results with an accuracy of 98%.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

001.43

Penerbit

: MDPI., 2022

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

001.43

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

Diabetes
ensemble bagged trees
machine learning
photoplethysmography
blood glucose levels

Info Detail Spesifik

Publikasi pada Jurnal Information 2022, 13, 59. https:// doi.org/10.3390/info13020059; kdag200523-9

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