Support Vector Regression based insulin dose forecasting for type II diabetes patients

In this paper, we compared the performance of Support Vector Regression (SVR), based insulin dose forecasting for type II diabetes patients, with Artificial Neural Network (ANN) learning with BackPropagation, Conjugate Gradient Descent, Levenberg-Marquardt, Quasi Newton and Quick Propagation respect...

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Main Authors: Pongsak Holimchayachotikul, Raweeroj Jintawiwat, Komgrit Leksakul
Format: Conference Proceeding
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/60439
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-604392018-09-10T03:42:34Z Support Vector Regression based insulin dose forecasting for type II diabetes patients Pongsak Holimchayachotikul Raweeroj Jintawiwat Komgrit Leksakul Engineering In this paper, we compared the performance of Support Vector Regression (SVR), based insulin dose forecasting for type II diabetes patients, with Artificial Neural Network (ANN) learning with BackPropagation, Conjugate Gradient Descent, Levenberg-Marquardt, Quasi Newton and Quick Propagation respectively. The methodology of this study started from collecting data of diabetes patients from Chiang Mai Maharaj Hospital. A series of experiments have been conducted for six approaches. After the learning processes had been accomplished, a performance of SVR was compared with the others in term of mean absolute deviation (MAD). The experimental results suggest that SVR can be dramatically trained in a shorter time than the others. In addition, insulin dose level, calculated by all of six approaches close to insulin level, was controlled by doctor. Moreover, SVR also provided the most robust among these five approaches of ANN. © 2008 ICQR. 2018-09-10T03:42:34Z 2018-09-10T03:42:34Z 2008-01-01 Conference Proceeding 2-s2.0-84906998294 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84906998294&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/60439
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Engineering
spellingShingle Engineering
Pongsak Holimchayachotikul
Raweeroj Jintawiwat
Komgrit Leksakul
Support Vector Regression based insulin dose forecasting for type II diabetes patients
description In this paper, we compared the performance of Support Vector Regression (SVR), based insulin dose forecasting for type II diabetes patients, with Artificial Neural Network (ANN) learning with BackPropagation, Conjugate Gradient Descent, Levenberg-Marquardt, Quasi Newton and Quick Propagation respectively. The methodology of this study started from collecting data of diabetes patients from Chiang Mai Maharaj Hospital. A series of experiments have been conducted for six approaches. After the learning processes had been accomplished, a performance of SVR was compared with the others in term of mean absolute deviation (MAD). The experimental results suggest that SVR can be dramatically trained in a shorter time than the others. In addition, insulin dose level, calculated by all of six approaches close to insulin level, was controlled by doctor. Moreover, SVR also provided the most robust among these five approaches of ANN. © 2008 ICQR.
format Conference Proceeding
author Pongsak Holimchayachotikul
Raweeroj Jintawiwat
Komgrit Leksakul
author_facet Pongsak Holimchayachotikul
Raweeroj Jintawiwat
Komgrit Leksakul
author_sort Pongsak Holimchayachotikul
title Support Vector Regression based insulin dose forecasting for type II diabetes patients
title_short Support Vector Regression based insulin dose forecasting for type II diabetes patients
title_full Support Vector Regression based insulin dose forecasting for type II diabetes patients
title_fullStr Support Vector Regression based insulin dose forecasting for type II diabetes patients
title_full_unstemmed Support Vector Regression based insulin dose forecasting for type II diabetes patients
title_sort support vector regression based insulin dose forecasting for type ii diabetes patients
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84906998294&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/60439
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