%0 Journal Article
%T Intelligent Systems Developed for the Early Detection of Chronic Kidney Disease
%A Ruey Kei Chiu
%A Renee Y. Chen
%A Shin-An Wang
%A Yen-Chun Chang
%A Li-Chien Chen
%J Advances in Artificial Neural Systems
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/539570
%X This paper aims to construct intelligence models by applying the technologies of artificial neural networks including back-propagation network (BPN), generalized feedforward neural networks (GRNN), and modular neural network (MNN) that are developed, respectively, for the early detection of chronic kidney disease (CKD). The comparison of accuracy, sensitivity, and specificity among three models is subsequently performed. The model of best performance is chosen. By leveraging the aid of this system, CKD physicians can have an alternative way to detect chronic kidney diseases in early stage of a patient. Meanwhile, it may also be used by the public for self-detecting the risk of contracting CKD. 1. Introduction According to the statistical data announced by the Department of Health of Taiwan¡¯s government in 2010 [1], the mortality caused by kidney disease has been ranked in the 10th place in all causes of death in Taiwan and thousands of others are at increased risk. The mortality caused from kidney disease is estimated as 12.5 in every 100,000 people. As a result, it costs as high as 35 percent of health insurance budget to treat the chronic kidney disease (CKD) patients with the age over 65 years old and end-stage kidney disease patients in all ages. It occupies a huge amount of expenditures in national insurance budget. Regarding the measurement of serious levels of CKD, presently glomerular filtration rate (GFR) is the most commonly measuring indicator used in health institutions to estimate kidney health function. The physician in the health institution can calculate GFR from patient¡¯s blood creatinine, age, race, gender, and other factors depending upon the type of formal-recognized computation formulas [2, 3] employed. The GFR may indicate the health of a patent¡¯s kidney and can also be taken to determine the stage of severity of a patient with or without kidney disease. In this paper, we aim to develop a feasible intelligent model for detecting CKD for evaluating the severity of a patient with or without CKD. The input data for model development and testing is collected from the health examination which is periodically carried out by the collaborative teaching hospital of this research. 2. The Major Methods for Measuring Chronic Kidney Disease As it is mentioned in prior section, the GFR is the most common method used to measure kidney health function. It refers to the water filterability of glomerular of people¡¯s kidney. The normal value should be between 90 and 120£¿mL/min/1.73£¿m2 (i.e., measured by mL per minute per 1.73£¿m2). There are three
%U http://www.hindawi.com/journals/aans/2013/539570/