Feed-forward neural network model for hunger and satiety related VAS score prediction

Krishnan, Shaji; Hendriks, H.; Hartvigsen, M L; Graaf, Albert A. de

doi:10.1186/s12976-016-0043-4

Cited by 11 publications

(12 citation statements)

References 32 publications

(25 reference statements)

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“…For the aim of predicting diabetes, a multilayer FNN was built using Python, Keras Toolbox™, R2013b [32,33]. Data were divided for a training set (80%) and testing set (20%).…”

Section: Multilayer Fnnmentioning

confidence: 99%

“…The aim of the training is to estimate the weights and bias values at every node of the network such that the trained network satisfactorily relates every input-output data from the training set. Such a trained multilayer FNN is capable to compute a unique output for wide range of inputs [32,33,37].…”

Section: Multilayer Fnnmentioning

confidence: 99%

“…Thus effectively, U and Y, both vectors, were used to train the multilayer FNN model in predicting the diabetes status from other different inputs. The model with an input, an output and two hidden layers was trained with sufficient amount of input-output datasets (~132 samples) will instantly predict the diabetes status for an arbitrary set of input values measured in time [32,33,[37][38][39]. The model was then used to predict the output of the testing list.…”

Section: Multilayer Fnnmentioning

confidence: 99%

“…The artificial neural network (i.e., multilayer FNN) provides an appropriate platform for modeling the complex input-output relationship [32,33,[37][38][39] between clinical (lipid profile), genetic (gene polymorphisms) and demographic (age and gender) factors and diabetes. Application of multilayer FNN to predict diabetes produced satisfactory results.…”

Section: Prediction Of Diabetes Status Using Fnnmentioning

confidence: 99%

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Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on VDR Gene FokI Polymorphism, Lipid Profile and Demographic Data

Hatmal

Abderrahman

Nimer

et al. 2020

Biology

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Type 2 diabetes mellitus (T2DM) is a multifactorial disease associated with many genetic polymorphisms; among them is the FokI polymorphism in the vitamin D receptor (VDR) gene. In this case-control study, samples from 82 T2DM patients and 82 healthy controls were examined to investigate the association of the FokI polymorphism and lipid profile with T2DM in the Jordanian population. DNA was extracted from blood and genotyped for the FokI polymorphism by polymerase chain reaction (PCR) and DNA sequencing. Lipid profile and fasting blood sugar were also measured. There were significant differences in high-density lipoprotein (HDL) cholesterol and triglyceride levels between T2DM and control samples. Frequencies of the FokI polymorphism (CC, CT and TT) were determined in T2DM and control samples and were not significantly different. Furthermore, there was no significant association between the FokI polymorphism and T2DM or lipid profile. A feed-forward neural network (FNN) was used as a computational platform to predict the persons with diabetes based on the FokI polymorphism, lipid profile, gender and age. The accuracy of prediction reached 88% when all parameters were included, 81% when the FokI polymorphism was excluded, and 72% when lipids were only included. This is the first study investigating the association of the VDR gene FokI polymorphism with T2DM in the Jordanian population, and it showed negative association. Diabetes was predicted with high accuracy based on medical data using an FNN. This highlights the great value of incorporating neural network tools into large medical databases and the ability to predict patient susceptibility to diabetes.

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“…For the aim of predicting diabetes, a multilayer FNN was built using Python, Keras Toolbox™, R2013b [32,33]. Data were divided for a training set (80%) and testing set (20%).…”

Section: Multilayer Fnnmentioning

confidence: 99%

Section: Multilayer Fnnmentioning

confidence: 99%

Section: Multilayer Fnnmentioning

confidence: 99%

Section: Prediction Of Diabetes Status Using Fnnmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on VDR Gene FokI Polymorphism, Lipid Profile and Demographic Data

Hatmal

Abderrahman

Nimer

et al. 2020

Biology

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show abstract

“…Visual analog scales are widely used in human research to evaluate appetite and mood (Parker et al, 2004;Krishnan et al, 2016). However, this approach could not be applied to animals due to the fact that animal cannot express their magnitude of feeling.…”

Section: Predictors Of Hunger Statusmentioning

confidence: 99%

Plasma acyl ghrelin and nonesterified fatty acids are the best predictors for hunger status in pregnant gilts1

Ren

Yang

Kim

et al. 2017

Journal of Animal Science

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Sows are usually restricted fed during pregnancy to maximize their reproductive efficiency, which may predispose sows to a state of hunger. However, an objective measurement of hunger status has not been established. In the present study, we examined the correlation of plasma hormones and NEFA and selected the best predictors for hunger status using pregnant gilts. Three different levels of feed intake (0.5, 1.0 and 2.0 × maintenance energy intake [0.5M, 1.0M and 2.0M, respectively]) were imposed from Day 28 to 34 of gestation to create different hunger statuses in pregnant gilts. Plasma hormones related to energy homeostasis and NEFA were analyzed to quantify their response to different levels of feed intake. A total of 18 gilts (197.53 ± 6.41 kg) were allotted to 1 of 3 dietary treatments using a completely randomized design. Results showed that BW change, ADG, and G:F from Day 28 to 34 of gestation were higher ( < 0.01) for gilts on the 2.0M feeding level than for gilts on the 0.5M feeding level. Plasma acyl ghrelin concentrations showed a relatively flat pattern during the 24-h period. Plasma acyl ghrelin and NEFA concentrations and areas under the curve (AUC) were greater ( < 0.05) in gilts on the 0.5M level of feed intake than in those on the 2.0M level of feed intake. No differences were observed among the 3 feeding levels in terms of plasma glucagon-like peptide 1 and leptin concentrations. Additionally, consumption time for 1.82 kg feed on Day 35 of gestation was longer ( < 0.01) in gilts fed the 2.0M level of feed intake from Day 28 to 34 of gestation than in those on the 0.5M level of feed intake. Simple linear regression results showed that the AUC of acyl ghrelin was the best predictor for consumption time ( = 0.82), whereas the AUC of NEFA was the best predictor for BW ( = 0.55) or backfat change ( = 0.42) from Day 28 to 34 of gestation. In conclusion, our data suggested that a relative flat pattern existed in pregnant gilts in terms of the diurnal plasma profile of acyl ghrelin and that the level of feed intake of pregnant gilts was negatively correlated with plasma concentrations of acyl ghrelin and NEFA, which, in turn, were negatively associated with feed consumption time. The AUC of acyl ghrelin and NEFA seemed to be the best predictors for hunger status of pregnant gilts.

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Application of artificial neural networks (ANN) for predicting the effect of processing on the digestibility of foods

Sandoval

Polanía

Florez

et al. 2023

Food Structure Engineering and Design for Improved Nutrition, Health and Well-Being

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Feed-forward neural network model for hunger and satiety related VAS score prediction

Cited by 11 publications

References 32 publications

Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on VDR Gene FokI Polymorphism, Lipid Profile and Demographic Data

Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on VDR Gene FokI Polymorphism, Lipid Profile and Demographic Data

Plasma acyl ghrelin and nonesterified fatty acids are the best predictors for hunger status in pregnant gilts1

Application of artificial neural networks (ANN) for predicting the effect of processing on the digestibility of foods

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