scholarly journals The Application of Artificial Neural Networks and Logistic Regression in the Evaluation of Risk for Dry Eye after Vitrectomy

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Wan-Ju Yang ◽  
Li Wu ◽  
Zhong-Ming Mei ◽  
Yi Xiang
Keyword(s):  
Logistic Regression ◽  
Clinical Data ◽  
Dry Eye ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural

Supervised machine-learning (ML) models were employed to predict the occurrence of dry eye disease (DED) after vitrectomy in this study. The clinical data of 217 patients receiving vitrectomy from April 2017 to July 2018 were used as training dataset; the clinical data of 33 patients receiving vitrectomy from August 2018 to September 2018 were collected as validating dataset. The input features for ML training were selected based on the Delphi method and univariate logistic regression (LR). LR and artificial neural network (ANN) models were trained and subsequently used to predict the occurrence of DED in patients who underwent vitrectomy for the first time during the period. The area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate the predictive accuracy of the ML models. The AUCs with use of the LR and ANN models were 0.741 and 0.786, respectively, suggesting satisfactory performance in predicting the occurrence of DED. When the two models were compared in terms of predictive power, the fitting effect of the ANN model was slightly superior to that of the LR model. In conclusion, both LR and ANN models may be used to accurately predict the occurrence of DED after vitrectomy.

Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil's Federal District

2019 ◽  
Vol 28 (1) ◽  
pp. 35 ◽  
Author(s):  
Pablo Pozzobon de Bem ◽  
Osmar Abílio de Carvalho Júnior ◽  
Eraldo Aparecido Trondoli Matricardi ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes
Keyword(s):  
Logistic Regression ◽  
Characteristic Curve ◽  
Federal District ◽  
Burned Area ◽  
Ann Model ◽  
Wildfire Management ◽  
Explanatory Variables ◽  
Burned Areas ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. In this work, we applied two data-mining models commonly used to predict fire occurrence – logistic regression (LR) and an artificial neural network (ANN) – to Brazil’s Federal District, located inside the Brazilian Cerrado. We used Landsat-based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors as explanatory variables. The models were optimised via feature selection for best area under receiver operating characteristic curve (AUC) and then validated with real burn area data. The models had similar performance, but the ANN model showed better AUC (0.77) and accuracy values when evaluating exclusively non-burned areas (73.39%), whereas it had worse accuracy overall (66.55%) when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. The main driving aspects of the burned area distribution were land-use type and elevation. The results showed good performance for both models tested. These studies are still scarce despite the importance of the Brazilian savanna.

scholarly journals Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

2021 ◽  
Vol 18 (10) ◽  
pp. 5110
Author(s):  
Shu-Farn Tey ◽  
Chung-Feng Liu ◽  
Tsair-Wei Chien ◽  
Chin-Wei Hsu ◽  
Kun-Chen Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Characteristic Curve ◽  
Patient Discharge ◽  
Ann Model ◽  
Healthcare Settings ◽  
High Area ◽  
Icd 10 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Development and validation of a clinical and genetic model for predicting risk of severe COVID-19

2021 ◽  
Author(s):  
Gillian S. Dite ◽  
Nicholas M. Murphy ◽  
Richard Allman
Keyword(s):  
Logistic Regression ◽  
Genetic Model ◽  
Characteristic Curve ◽  
Accurate Prediction ◽  
Training Dataset ◽  
Validation Dataset ◽  
Individual Risk ◽  
Prototype Model ◽  
List Type ◽  
New Model

SummaryClinical and genetic risk factors for severe COVID-19 are often considered independently and without knowledge of the magnitudes of their effects on risk. Using SARS-CoV-2 positive participants from the UK Biobank, we developed and validated a clinical and genetic model to predict risk of severe COVID-19. We used multivariable logistic regression on a 70% training dataset and used the remaining 30% for validation. We also validated a previously published prototype model. In the validation dataset, our new model was associated with severe COVID-19 (odds ratio per quintile of risk=1.77, 95% confidence interval [CI]=1.64, 1.90) and had excellent discrimination (area under the receiver operating characteristic curve=0.732, 95% CI=0.708, 0.756). We assessed calibration using logistic regression of the log odds of the risk score, and the new model showed no evidence of over- or under-estimation of risk (α=−0.08; 95% CI=−0.21, 0.05) and no evidence or over- or under-dispersion of risk (β=0.90, 95% CI=0.80, 1.00). Accurate prediction of individual risk is possible and will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.Key resultsAccurate prediction of the risk of severe COVID-19 can inform public heath interventions and empower individuals to make informed choices about their day-to-day activities.Age and sex alone do not accurately predict risk of severe COVID-19.Our clinical and genetic model to predict risk of severe COVID-19 performs extremely well in terms of discrimination and calibration.

scholarly journals Predictive Analysis of Employee Loyalty: A Comparative Study Using Logistic Regression Model and Artificial Neural Network

2019 ◽  
Vol 25 (3) ◽  
pp. 325-335
Author(s):  
Maria Zefanya Sampe ◽  
Eko Ariawan ◽  
I Wayan Ariawan
Keyword(s):  
Logistic Regression ◽  
Employee Turnover ◽  
Mean Squared Error ◽  
Working Hours ◽  
Retention Rates ◽  
Ann Model ◽  
Employee Loyalty ◽  
High Turnover ◽  
Kolmogorov Smirnov ◽  
Artificial Neural

Employee turnover is a common issue in any company. A high turnover phenomenon becomes a big problem that will certainly affect the performance of the company. Therefore, measuring employee turnover can be helpful to employers to improve employee retention rates and give them a head start on turnover. A study to analyze for employee loyalty has been carried out by using Logistic Regression (LR) and Artificial Neural Networks (ANN) model. Response variables such as satisfaction level, number of projects, average monthly working hours, employment period, working accident, promotion in the last 5 years, department, and salary level are used to model the employee turnover. Parameters such as accuracy, precision, sensitivity, Kolmogorov-Smirnov statistic, and Mean Squared Error (MSE) are used to compare both models.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

scholarly journals Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2390 ◽  
Author(s):  
Olalekan Alade ◽  
Dhafer Al Shehri ◽  
Mohamed Mahmoud ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heavy Oil ◽  
Atmospheric Pressure ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Viscosity Data ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 °C and 150 °C; and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 °C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 °C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 °C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 °C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. Moreover, compared to other empirical models, statistical analysis shows that the ANN model has a better predictive accuracy (R2 ≈ 1) for the viscosity data of the heavy oil samples used in this study.

scholarly journals Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks

Pharmaceutics ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 400 ◽  
Author(s):  
Galata ◽  
Farkas ◽  
Könyves ◽  
Mészáros ◽  
Szabó ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Extended Release ◽  
Near Infrared ◽  
Hydroxypropyl Methylcellulose ◽  
In Vitro Dissolution ◽  
Dissolution Profile ◽  
Ann Model ◽  
Compression Force ◽  
Ann Models ◽  
Artificial Neural

The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine (DR) as a model drug was developed and tablets were produced with 37 different settings, with the variables being the DR content, the hydroxypropyl methylcellulose (HPMC) content and compression force. NIR and Raman spectra of the tablets were recorded in both the transmission and reflection method. The spectra were used to build a partial least squares prediction model for the DR and HPMC content. The ANN model used these predicted values, along with the measured compression force, as input data. It was found that models based on both NIR and Raman spectra were capable of predicting the dissolution profile of the test tablets within the acceptance limit of the f2 difference factor. The performance of these ANN models was compared to PLS models using the same data as input, and the prediction of the ANN models was found to be more accurate. The proposed method accomplishes the prediction of the dissolution profile of extended release tablets using either NIR or Raman spectra.

Modeling of the Stress in 13Cr Supermartensitic Stainless Steel Welds by Artificial Neural Network

2010 ◽  
Vol 658 ◽  
pp. 141-144 ◽  
Author(s):  
Jun Hui Yu ◽  
De Ning Zou ◽  
Ying Han ◽  
Zhi Yu Chen
Keyword(s):  
Stainless Steel ◽  
Correlation Coefficients ◽  
Tensile Tests ◽  
Training Dataset ◽  
Ann Model ◽  
Supermartensitic Stainless Steel ◽  
Absolute Relative Error ◽  
Feed Forward Network ◽  
Artificial Neural ◽  
Hidden Layer

In this paper, artificial neural networks (ANN) has been proposed to determine the stresses of 13Cr supermartensitic stainless steel (SMSS) welds based on various deformation temperatures and strains using experimental data from tensile tests. The experiments provided the required data for training and testing. A three layer feed-forward network, deformation temperature and strain as input parameters while stress as the output, was trained with automated regularization (AR) algorithm for preventing overfitting. The results showed that the best fitting training dataset was obtained with ten units in the hidden layer, which made it possible to predict stress accurately. The correlation coefficients (R-value) between experiments and prediction for the training and testing dataset were 0.9980 and 0.9943, respectively, the biggest absolute relative error (ARE) was 6.060 %. As seen that the ANN model was an efficient quantitative tool to evaluate and predict the deformation behavior of type 13Cr SMSS welds during tensile test under different temperatures and strains.

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