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Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis
2015
Medical Journal of The Islamic Republic of Iran
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for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. ...
In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. ...
Acknowledgments This article is part of a research project titled "The Comparison of Logistic Regression, Probit, and Linear Discriminant Analysis Models in the Prediction of Unwanted Pregnancies in Khorramabad ...
pmid:26793655
pmcid:PMC4715395
fatcat:qi4v4jwmczgq3edunujztimm5m
Comparison of artificial neural network and logistic regression model for factors affecting birth weight
2019
SN Applied Sciences
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The aim of this work compares the ANN and logistic regression analysis to determine the factors affecting birth weight. This study included 223 newborn babies. ...
ANN and logistic regression analysis of the method obtained based on these records were evaluated. ...
Type of analysis
Artificial neural networks (ANNs) ANNs, one of the research fields of artificial intelligence science, includes studies on learning computers. ...
doi:10.1007/s42452-019-0391-x
fatcat:yzkqvcvwxrcapfa224el5kiar4
The Application of Multinomial Logistic Regression Models for the Assessment of Parameters of Oocytes and Embryos Quality in Predicting Pregnancy and Miscarriage
2017
Studies in Logic, Grammar and Rhetoric
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In a situation when a phenomenon with more than 2 states needs to be explained, e.g. pregnancy, miscarriage, non-pregnancy, the use of multinomial logistic regression is a good solution. ...
One of the models enabled to conclude that the number of follicles and the percentage of retrieved mature oocytes have a significant impact when prediction of treatment outcome is made on the basis of ...
The obtained results were compared with the classifier created on the basis of a neural network. ...
doi:10.1515/slgr-2017-0030
fatcat:xwjgtgvxunchrciku2an57wmya
Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods
2017
Osong Public Health and Research Perspectives
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The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. ...
To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. ...
Several classification methods can be used to classify a new case into one of the response categories, including logistic regression (LR), decision trees (DTs), artificial neural networks, genetic algorithms ...
doi:10.24171/j.phrp.2017.8.3.06
pmid:28781942
pmcid:PMC5525564
fatcat:twsu677azjgw7e7enzo4ilqfia
A robust voting approach for diabetes prediction using traditional machine learning techniques
2019
SN Applied Sciences
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Here, eleven well-known machine-learning algorithms like Naïve Bayes, K-NN, SVM, Random Forest, Artificial Neural Network, Logistic Regression, Gradient Boosting, Ada Boosting etc. are used for detection ...
The point of the present examination is to direct an orderly audit of the uses of machine-learning, data mining strategies and instruments in the field of diabetes. ...
Faruque Hossain from the Department of Electronics and Communication Engineering at Khulna University of Engineering and Technology who helped me throughout the editing and formatting. ...
doi:10.1007/s42452-019-1759-7
fatcat:2ctjf6bj7faeveubuljcqvstji
Investigating the Components of Virtual Emergency Department
[chapter]
2022
Studies in Health Technology and Informatics
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The prediction of the demography of Spain shows that Spain will experience an aging population soon. Aging is a condition of chronic disease resulting in overcrowding Emergency Department. ...
We have proposed a model for this integrated model and studied the possibility of success in each step including admission, triage, diagnoses, and clinical advice based on literature. ...
In the second one, seven classifiers, including Naïve Bayes, Bayesian network with the K2 algorithm, Efficient Bayesian Multivariate Classification, Artificial Neural Networks, Logistic Regression, SVM ...
doi:10.3233/shti220012
pmid:35593761
fatcat:a7ovhdzqlza6nl26uuq42pu724
Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study
2021
Sensors
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In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms ...
and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms. ...
Concurrently, this study has also highlighted a reduction in the percentage of studies that used statistical regression algorithms coupled with an increased percentage of studies that used neural network-based ...
doi:10.3390/s22010052
pmid:35009593
pmcid:PMC8747441
fatcat:mnf3ts7os5ffrnj6ijbuzfqrmm
A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows
2017
Journal of Dairy Science
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The neural network and multinomial regression models developed are both suitable for use in decision-support and simulation modeling. ...
Only multinomial regression and neural networks explicitly included the modeled interactions. ...
The authors thank Anne Geoghegan of Teagasc Moorepark (Ireland) for assistance in gathering the data used in this study. ...
doi:10.3168/jds.2017-12931
pmid:28941818
fatcat:74kdc6zfsjbvhjiqjqo7oj36we
Early detection of Parkinson's disease using image processing and artificial neural network
2018
2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR)
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To improve the imaging diagnosis of PD, we propose a model in this paper, for early detection of Parkinson"s disease using Image Processing and Artificial Neural Network (ANN). ...
which is the Region of Interest (ROI) for this study. ...
Artificial Neural Network With the obtained data that is the area of Putamen and Caudate of left and right hemisphere of the brain we train a prediction model using Artificial Neural Network. ...
doi:10.1109/iciev.2018.8641081
fatcat:5pnwf557a5ganixacmapkbeacy
Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition
2016
Journal of Applied Statistics
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The working group (WG) CMStatistics comprises a number of specialized teams in various research areas of computational and methodological statistics. ...
methods for Predictive and Exploratory Path modeling ...
On the basis of a simulation study, it is studied the effect of unmeasured population heterogeneity on estimates of the distribution of time to pregnancy and of the effect of risk factors. ...
doi:10.1080/02664763.2016.1263835
fatcat:l5eyielgxrct7hq5ljqeej5ccy
Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review
2020
Chaos, Solitons & Fractals
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Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of ...
To manage the problems, many research in a variety of area of science have started studying the issue. ...
Then Logistic Regression, Multinomial Naive Bayes are used to classify the data. ...
doi:10.1016/j.chaos.2020.110338
pmid:33041533
pmcid:PMC7532790
fatcat:gl3i37hag5gflajsa7fh6khvva
Neural translation and automated recognition of ICD10 medical entities from natural language
[article]
2020
arXiv
pre-print
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of neural sequence models and their powerful applications in natural language processing. ...
The recent advances in artificial intelligence, specifically the raise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems, with the notable example ...
statistical modelling problems, and could be solved using multinomial logistic regression. ...
arXiv:2004.13839v2
fatcat:v3ff7kwp7vdtdhsdxma66gp6qa
Exploring the impact of knowledge and social environment on influenza prevention and transmission in Midwestern United States high school students
2012
European Journal of Health and Biology Education
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We would finally like to acknowledge the significant contributions of Stephanie Won, Mekka Garcia, and Elmer Rho to prior research which served as a foundation for the present study. ...
ACKNOWLEDGEMENTS We would like to thank the Howard Hughes medical institute for funding this project. We We appreciate the helpful comments of two anonymous reviewers and Garet Marling. ...
Since both types of relationships were of interest, variables with statistically significant r or ρ values were considered for inclusion in the logistic regression and neural network models. ...
doi:10.20897/lectito.201205
fatcat:kl6izbwn2fhbrp2h77dmt26r4a
Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes
2021
Briefings in Bioinformatics
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Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). ...
The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy ...
logistic regression (LR) [71] . ...
doi:10.1093/bib/bbaa369
pmid:33406530
pmcid:PMC8424395
fatcat:rgsf4pdmbvdcdmcwz4zf3hwaca
Robust Machine Learning for Colorectal Cancer Risk Prediction and Stratification
2020
Frontiers in Big Data
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decision tree, random forest, logistic regression, and artificial neural network. ...
Among all of the model configurations and imputation method combinations, the artificial neural network with expectation-maximization imputation emerged as the best, having a concordance of 0.70 ± 0.02 ...
ACKNOWLEDGMENTS The authors gratefully acknowledge the facilities provided by the Yale Department of Therapeutic Radiology at which this work was carried out. ...
doi:10.3389/fdata.2020.00006
pmid:33693381
pmcid:PMC7931964
fatcat:gmunloykhzbkdnzyjoc37kz5ee
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