Design and Algorithms of the Device to predict Blood Glucose Level based on Saliva Sample using Machine Learning

Jahagirdar, Sumit; Sharma, Vivank

doi:10.1109/icssit46314.2019.8987866

Cited by 4 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is not necessarily a drawback, as it could be applied in a qualitative binary-response sensor which, among parallel colorimetric data in the whole of a multi-sensing device, would be a valuable add on. Jahagirdar et al [ 105 ] present a ML-based spectrophotometric device for the prediction of glucose level in blood from saliva samples that is low-cost, thus easing its widespread use.…”

Section: Sample Fluidmentioning

confidence: 99%

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy—Application of Sensor Arrays and Machine Learning

Faura

Boix‐Lemonche

Holmeide

et al. 2022

Sensors

View full text Add to dashboard Cite

In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.

show abstract

Section: Sample Fluidmentioning

confidence: 99%