Soft computing approach for predictive blood glucose management using a fuzzy neural network

被引：0

作者：

Mathiyazhagan, Nithyanandam ^{[1
]}

Schechter, Howard B. ^{[1
]}

机构：

[1] Walden Univ, Minneapolis, MN USA

来源：

2014 IEEE CONFERENCE ON NORBERT WIENER IN THE 21ST CENTURY (21CW) | 2014年

关键词：

Soft computing; adaptive network-based fuzzy inference system (ANFIS); Type 1 Diabetes (T1D); fuzzy logic; artificial neural networks (ANN); INFERENCE SYSTEM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Challenges in the management of blood sugar for Type 1 Diabetes mellitus (T1DM) patients have emerged to be one of the major contributors for the increase in societal cost of the health care system. The objective of this work is to develop and assess a computerized model for predicting blood glucose in patients with T1DM using the insulin pump and continuous glucose sensor. This study draws upon a soft computing approach that tolerates imprecision, uncertainty, and partial truth. An adaptive network-based fuzzy inference system (ANFIS) was implemented in the framework of fuzzy inferences and adaptive networks using an artificial neural network. The goal for the predictive approach is to provide personalized aid to patients with T1DM to better manage their glucose levels.

引用

页数：3

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