Comparison of artificial neural networks with logistic regression in prediction of gallbladder disease among obese patients

Liew, Phui Ly; Lee, Y.-C.; Lin, Yang; Lee, T.-S.; Lee, W.-J.; Wang, W.; Chien, Ching Wen

doi:10.1016/j.dld.2007.01.003

Cited by 33 publications

(26 citation statements)

References 29 publications

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“…All eight validation projects were classified correctly, and thus our model is a preferable choice for predicting whether a project is risky compared with LR. These results are also consistent with previous studies [24,25].…”

Section: Comparison Among Performancessupporting

confidence: 96%

Discriminating risky software project using neural networks

Han¹

2015

Computer Standards & Interfaces

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Section: Comparison Among Performancessupporting

confidence: 96%

Discriminating risky software project using neural networks

Han¹

2015

Computer Standards & Interfaces

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“…Association of GD to metabolic syndrome includes obesity, type II diabetes mellitus, dyslipidemia and insulin resistance [11][12][13][14]. A pathogenic link between low-grade systemic chronic inflammation, obesity, insulin resistance, NASH, and GD is interesting.…”

Section: Introductionmentioning

confidence: 98%

Fatty Liver Disease: Predictors of Nonalcoholic Steatohepatitis and Gallbladder Disease in Morbid Obesity

et al. 2008

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The prevalence of NASH in gallbladder disease was 18% in morbid obese population. We concluded that age, serum cholesterol, and low-density lipoprotein cholesterol levels were risk factors associated with gallbladder disease and fatty liver disease. Insulin resistance was more common in concurrent NASH and gallbladder disease. The mechanism between insulin resistance, fibrosis stage, NASH, and gallbladder disease is unknown.

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“…They came to the conclusion that ANNs might be a useful tool for predicting the risk factors and prevalence of gallbladder disease and gallstone development in obese patients (17). Tsipouras and coworkers applied DM methods for the analysis of cardiac disease and electrocardiogram interpretations.…”

Section: Articles Grigull and Lechnermentioning

confidence: 99%

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Grigull

Lechner²

2012

Pediatr Res

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IntroductIon: This article demonstrates the capacity of a combination of different data mining (DM) methods to support diagnosis in pediatric emergency patients. By using a novel combination of these DM procedures, a computer-based diagnosis was created. methods: a support vector machine (sVM), artificial neural networks (aNNs), fuzzy logics, and a voting algorithm were simultaneously used to allocate a patient to one of 18 diagnoses (e.g., pneumonia, appendicitis). anonymized data sets of patients who presented in the emergency department (eD) of a pediatric care clinic were chosen. For each patient, 26 identical clinical and laboratory parameters were used (e.g., blood count, c-reactive protein) to finally develop the program. results: The combination of four DM operations arrived at a correct diagnosis in 98% of the cases, retrospectively. a subgroup analysis showed that the highest diagnostic accuracy was for appendicitis (97% correct diagnoses) and idiopathic thrombocytopenic purpura or erythroblastopenia (100% correct diagnoses). During the prospective testing, 81% of the patients were correctly diagnosed by the system. dIscussIon: The combination of these DM methods was suitable for proposing a diagnosis using both laboratory and clinical parameters. We conclude that an optimized combination of different but complementary DM methods might serve to assist medical decisions in the eD.

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Comparison of artificial neural networks with logistic regression in prediction of gallbladder disease among obese patients

Cited by 33 publications

References 29 publications

Discriminating risky software project using neural networks

Discriminating risky software project using neural networks

Fatty Liver Disease: Predictors of Nonalcoholic Steatohepatitis and Gallbladder Disease in Morbid Obesity

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

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