Prediction of preterm birth in nulliparous women using logistic regression and machine learning

Objective To predict preterm birth in nulliparous women using logistic regression and machine learning. Design Population-based retrospective cohort. Participants Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20–42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014. Methods We used data during the first and second trimesters to build logistic regression and machine learning models in a “training” sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent “validation” sample. Results During the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23–5.42; Type II: AOR: 2.68; 95% CI: 2.05–3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80–2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58–62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21–13.90). During the second trimester, the AUC increased to 65% (95% CI: 63–66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79–81%) with artificial neural networks. All models yielded 94–97% negative predictive values for spontaneous PTB during the first and second trimesters. Conclusion Although artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.

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A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽

10.3390/s21051654 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1654

Author(s):

Poojitha Vurtur Badarinath ◽

Maria Chierichetti ◽

Fatemeh Davoudi Kakhki

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Finite Element Analysis ◽

Finite Element ◽

Artificial Neural Networks ◽

Stress Distribution ◽

Maintenance Scheduling ◽

Element Analysis ◽

One Dimensional ◽

Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

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An Immersive Node-Link Visualization of Artificial Neural Networks for Machine Learning Experts

2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) ◽

10.1109/aivr50618.2020.00015 ◽

2020 ◽

Author(s):

Martin Bellgardt ◽

Christian Scheiderer ◽

Torsten W. Kuhlen

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Artificial Neural

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Artificial Neural Networks and Machine Learning – ICANN 2021

10.1007/978-3-030-86383-8 ◽

2021 ◽

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Artificial Neural

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Binary Fields and Economics through Fuzzy Logic Approach and Boolean Algebra Using Multidimensional Processing with Respect to Artificial Neural Networks and Machine Learning

SSRN Electronic Journal ◽

10.2139/ssrn.3123275 ◽

2016 ◽

Author(s):

Constantinos Challoumis

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Fuzzy Logic ◽

Artificial Neural Networks ◽

Boolean Algebra ◽

Binary Fields ◽

Fuzzy Logic Approach ◽

Logic Approach ◽

Artificial Neural

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Adaptive kinetic structural behavior through machine learning: Optimizing the process of kinematic transformation using artificial neural networks

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060415000396 ◽

2015 ◽

Vol 29 (4) ◽

pp. 371-391 ◽

Cited By ~ 3

Author(s):

Odysseas Kontovourkis ◽

Marios C. Phocas ◽

Ifigenia Lamprou

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Adaptive Behavior ◽

Physical Environment ◽

Transformation Process ◽

Artificial Neural ◽

Kinematic Transformation ◽

Alternative Solutions ◽

Digital Or

AbstractNowadays, on the basis of significant work carried out, architectural adaption structures are considered to be intelligent entities, able to react to various internal or external influences. Their adaptive behavior can be examined in a digital or physical environment, generating a variety of alternative solutions or structural transformations. These are controlled through different computational approaches, ranging from interactive exploration ones, producing alternative emergent results, to automate optimization ones, resulting in acceptable fitting solutions. This paper examines the adaptive behavior of a kinetic structure, aiming to explore suitable solutions resulting in final appropriate shapes during the transformation process. A machine learning methodology that implements an artificial neural networks algorithm is integrated to the suggested structure. The latter is formed by units articulated together in a sequential composition consisting of primary soft mechanisms and secondary rigid components that are responsible for its reconfiguration and stiffness. A number of case studies that respond to unstructured environments are set as examples, to test the effectiveness of the proposed methodology to be used for handling a large number of input data and to optimize the complex and nonlinear transformation behavior of the kinetic system at the global level, as a result of the units’ local activation that influences nearby units in a chaotic and unpredictable manner.

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