Open AccessAccuracy of Predicted Glucose using both Natural Intelligence and Artificial Intelligence via GH-Method: Math-Physical Medicine (No. 320)
Author(s) -
Gerald C Hsu,
AUTHOR_ID
Publication year - 2021
Language(s) - English
DOI - 10.47363/jdrr/2021(3)144
Subject(s) - postprandial , plasma glucose , artificial intelligence , computer science , endocrinology , machine learning , medicine , diabetes mellitus
This paper describes the accuracy of using natural intelligence (NI) and artificial intelligence (AI) methods to predict three glucoses, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and daily average glucose, in comparison with the actual measured PPG by using the finger-piercing (Finger) method. The entire glucose database contains 7,652 glucoses (4 glucose data per day) over 1,913 days from 6/1/2015 through 8/27/2020