Predictors of Newborn’s Weight for Height: A Machine Learning Study Using Nationwide Multicenter Ultrasound Data

There has been no machine learning study with a rich collection of clinical, sonographic markers to compare the performance measures for a variety of newborns’ weight-for-height indicators. This study compared the performance measures for a variety of newborns’ weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. The source of data for this study was a multi-center retrospective study with 2949 mother–newborn pairs. The mean-squared-error-over-variance measures of five machine learning approaches were compared for newborn’s weight, newborn’s weight/height, newborn’s weight/height2 and newborn’s weight/hieght3. Random forest variable importance, the influence of a variable over average node impurity, was used to identify major predictors of these newborns’ weight-for-height indicators among ultrasonographic data and maternal/delivery information. Regarding ultrasonographic fetal biometry, newborn’s weight, newborn’s weight/height and newborn’s weight/height2 were better indicators with smaller mean-squared-error-over-variance measures than newborn’s weight/height3. Based on random forest variable importance, the top six predictors of newborn’s weight were the same as those of newborn’s weight/height and those of newborn’s weight/height2: gestational age at delivery time, the first estimated fetal weight and abdominal circumference in week 36 or later, maternal weight and body mass index at delivery time, and the first biparietal diameter in week 36 or later. These six predictors also ranked within the top seven for large-for-gestational-age and the top eight for small-for-gestational-age. In conclusion, newborn’s weight, newborn’s weight/height and newborn’s weight/height2 are more suitable for ultrasonographic fetal biometry with smaller mean-squared-error-over-variance measures than newborn’s weight/height3. Machine learning with ultrasonographic data would be an effective noninvasive approach for predicting newborn’s weight, weight/height and weight/height2.

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Prediction of newborn’s body mass index using nationwide multicenter ultrasound data: a machine-learning study

BMC Pregnancy and Childbirth ◽

10.1186/s12884-021-03660-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kwang-Sig Lee ◽

◽

Ho Yeon Kim ◽

Se Jin Lee ◽

Sung Ok Kwon ◽

...

Keyword(s):

Machine Learning ◽

Body Mass Index ◽

Random Forest ◽

Body Mass ◽

Gestational Age ◽

Variable Importance ◽

Abdominal Circumference ◽

Learning Study ◽

Gestational Age At Delivery ◽

Maternal Bmi

Abstract Background This study introduced machine learning approaches to predict newborn’s body mass index (BMI) based on ultrasound measures and maternal/delivery information. Methods Data came from 3159 obstetric patients and their newborns enrolled in a multi-center retrospective study. Variable importance, the effect of a variable on model performance, was used for identifying major predictors of newborn’s BMI among ultrasound measures and maternal/delivery information. The ultrasound measures included biparietal diameter (BPD), abdominal circumference (AC) and estimated fetal weight (EFW) taken three times during the week 21 - week 35 of gestational age and once in the week 36 or later. Results Based on variable importance from the random forest, major predictors of newborn’s BMI were the first AC and EFW in the week 36 or later, gestational age at delivery, the first AC during the week 21 - the week 35, maternal BMI at delivery, maternal weight at delivery and the first BPD in the week 36 or later. For predicting newborn’s BMI, linear regression (2.0744) and the random forest (2.1610) were better than artificial neural networks with one, two and three hidden layers (150.7100, 154.7198 and 152.5843, respectively) in the mean squared error. Conclusions This is the first machine-learning study with 64 clinical and sonographic markers for the prediction of newborns’ BMI. The week 36 or later is the most effective period for taking the ultrasound measures and AC and EFW are the best predictors of newborn’s BMI alongside gestational age at delivery and maternal BMI at delivery.

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Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes

Processes ◽

10.3390/pr9112095 ◽

2021 ◽

Vol 9 (11) ◽

pp. 2095

Author(s):

Ganesh N. ◽

Paras Jain ◽

Amitava Choudhury ◽

Prasun Dutta ◽

Kanak Kalita ◽

...

Keyword(s):

Machine Learning ◽

Fluid Flow ◽

Random Forest ◽

Mean Squared Error ◽

Flow Characteristics ◽

Accurate Estimation ◽

Random Forest Regression ◽

Curved Pipes ◽

Squared Error ◽

Computationally Expensive

In industrial piping systems, turbomachinery, heat exchangers etc., pipe bends are essential components. Computational fluid dynamics (CFD), which is frequently used to analyse the flow behaviour in such systems, provides extremely precise estimates but is computationally expensive. As a result, a computationally efficient method is developed in this paper by leveraging machine learning for such computationally expensive CFD problems. Random forest regression (RFR) is used as the machine learning algorithm in this work. Four different fluid flow characteristics (i.e., axial velocity, x-velocity, y-velocity and z-velocity) are studied in this work. The accuracy of the RFR models is assessed by using a number of statistical metrics such as mean-absolute error (MAE), mean-squared-error (MSE), root-mean-squared-error (RMSE), maximum error (Max.Error) and median error (Med.Error) etc. It is observed that the RFR models can produce considerable cost reductions in computing by surrogating the CFD model. Minor loss in estimation accuracy as compared to the CFD models is observed. While the magnitude of intricate flow characteristics such as the additional vortices are correctly predicted, some error in their location is observed.

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Predicting Future Products Rate using Machine Learning Algorithms

International Journal of Intelligent Systems and Applications ◽

10.5815/ijisa.2020.05.04 ◽

2020 ◽

Vol 12 (5) ◽

pp. 41-51

Author(s):

Shaimaa Mahmoud ◽

◽

Mahmoud Hussein ◽

Arabi Keshk

Keyword(s):

Machine Learning ◽

Random Forest ◽

Linear Regression ◽

Mean Squared Error ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Random Forest Regression ◽

Data Set ◽

Squared Error

Opinion mining in social networks data is considered as one of most important research areas because a large number of users interact with different topics on it. This paper discusses the problem of predicting future products rate according to users’ comments. Researchers interacted with this problem by using machine learning algorithms (e.g. Logistic Regression, Random Forest Regression, Support Vector Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and Decision Tree). However, the accuracy of these techniques still needs to be improved. In this study, we introduce an approach for predicting future products rate using LR, RFR, and SVR. Our data set consists of tweets and its rate from 1:5. The main goal of our approach is improving the prediction accuracy about existing techniques. SVR can predict future product rate with a Mean Squared Error (MSE) of 0.4122, Linear Regression model predict with a Mean Squared Error of 0.4986 and Random Forest Regression can predict with a Mean Squared Error of 0.4770. This is better than the existing approaches accuracy.

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Comparison of different Artificial Intelligence techniques to predict Diabetic Kidney Disease (Preprint)

10.2196/preprints.22636 ◽

2020 ◽

Author(s):

Satish Kumar ◽

Mohamed Rafiullah ◽

Khalid Siddiqui

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

High Risk ◽

Random Forest ◽

Diabetic Kidney Disease ◽

Mean Squared Error ◽

Confusion Matrix ◽

Classification Techniques ◽

Squared Error ◽

Classification Technique

BACKGROUND Diabetic kidney disease (DKD) is a progressive disease that leads to loss of kidney function. As early intervention improves patient outcomes, it is essential to identify the patients who are at high risk of developing DKD. Artificial Intelligence methods apply different machine learning classification techniques to identify high-risk patients by building a predictive model from a given dataset. OBJECTIVE This study aims to find an accurate classification technique for predicting DKD by comparing different classification techniques applied to a DKD dataset using WEKA machine learning software. METHODS We analyzed the performance of nine different classification techniques on a DKD dataset with 410 instances and 18 attributes. 66% of the dataset was used to build a model, and 33% of the data was used for evaluating the model. The performance of classification techniques were assessed based on their execution time, accuracy, correctly and incorrectly classified instances, kappa statistics (K), mean absolute error, root mean squared error and true values of the confusion matrix. RESULTS Random Forest classifier was found to be the best performing technique with an accuracy of 76.5854% and a higher K value (0.5306) in comparison to other classifiers. Besides, it also showed the lowest root mean squared error rate (0.4007). From the confusion matrix, it was found that there were 46 false-positive instances and 50 false-negative instances from the Random Forest technique. CONCLUSIONS This study identified the Random Forest classification technique as the best performing classifier and accurate prediction method for DKD. CLINICALTRIAL NA

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Um sistema baseado em machine learning para apoio à decisão no gerenciamento de produção apícola

REVISTA BRASILEIRA DE AGROTECNOLOGIA ◽

10.18378/rebagro.v11i1.8679 ◽

2021 ◽

Vol 11 (1) ◽

pp. 08-19

Author(s):

Weskley Damasceno Silva ◽

Silas Santiago Lopes Pereira ◽

Daniel Santiago Pereira ◽

Michell Olívio Xavier da Costa

Keyword(s):

Machine Learning ◽

Random Forest ◽

Decision Tree ◽

Support Vector Regression ◽

Multilayer Perceptron ◽

Mean Squared Error ◽

Support Vector ◽

Root Mean Squared Error ◽

Squared Error

O setor apícola tem ganhado grandes proporções nos últimos tempos em termos de produção e comercialização de produtos, como o mel e seus derivados. O Brasil, apesar de ter acompanhado esse crescimento e possuir boas características para o desenvolvimento da apicultura, ainda sofre com a limitação no uso de ferramentas tecnológicas, o que afeta diretamente os níveis de produção. Este artigo propõe o desenvolvimento de uma ferramenta tecnológica que auxilie o apicultor no gerenciamento eficiente da produção apícola e na tomada de decisão a partir de modelos preditivos baseados em Machine Learning (ML) e integrados a um sistema web. Para tanto, foram utilizados diferentes algoritmos de ML para predição de produção de mel, tais como a Regressão Linear Múltipla, Decision Tree, Random Forest, Multilayer Perceptron (MLP) e Support Vector Regression (SVR). Os modelos gerados foram avaliados com base no coeficiente de determinação (R2 ou Score) e o cálculo de erro das predições utilizando a Root Mean Squared Error (RMSE). Os resultados desta pesquisa contam com um sistema web em desenvolvimento e resultados dos experimentos realizados, que mostram uma melhor performance da técnica MLP com Score de 0.98 e RMSE de 711196 libras.

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Vorhersage der Fließgewässertemperaturen in österreichischen Einzugsgebieten mittels Machine Learning-Verfahren

Österreichische Wasser- und Abfallwirtschaft ◽

10.1007/s00506-021-00771-3 ◽

2021 ◽

Author(s):

Moritz Feigl ◽

Katharina Lebiedzinski ◽

Mathew Herrnegger ◽

Karsten Schulz

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Random Forest ◽

Recurrent Neural Networks ◽

Mean Squared Error ◽

Feedforward Neural Networks ◽

Gradient Boosting ◽

Squared Error ◽

Extreme Gradient Boosting ◽

Lineare Regression

ZusammenfassungDie Fließgewässertemperatur ist ein essenzieller Umweltfaktor, der das Potenzial hat, sowohl ökologische als auch sozio-ökonomische Rahmenbedingungen im Umfeld eines Gewässers zu verändern. Um Fließgewässertemperaturen als Grundlage für effektive Anpassungsstrategien für zukünftige Veränderungen (z. B. durch den Klimawandel) berechnen zu können, sind adäquate Modellierungskonzepte notwendig. Die vorliegende Studie untersucht hierfür 6 Machine Learning-Modelle: Schrittweise Lineare Regression, Random Forest, eXtreme Gradient Boosting, Feedforward Neural Networks und zwei Arten von Recurrent Neural Networks. Die Modelle wurden an 10 österreichischen Einzugsgebieten mit unterschiedlichen physiographischen Eigenschaften und Eingangsdatenkombinationen getestet. Die Hyperparameter der angewandten Modelle wurden mittels Bayes’scher Hyperparameteroptimierung optimiert. Um die Ergebnisse mit anderen Studien vergleichbar zu machen, wurden die Vorhersagen der 6 Machine Learning-Modelle den Ergebnissen der linearen Regression und dem häufig verwendeten und bekannten Wassertemperaturmodell air2stream gegenübergestellt.Von den 6 getesteten Modellen zeigten die Feedforward Neural Networks und das eXtreme Gradient Boosting die besten Vorhersagen in jeweils 4 von 10 Einzugsgebieten. Mit einem durchschnittlichen RMSE (Wurzel der mittleren Fehlerquadratsumme; root mean squared error) von 0,55 °C konnten die getesteten Modelle die Fließgewässertemperaturen deutlich besser prognostizieren als die lineare Regression (1,55 °C) und air2stream (0,98 °C). Generell zeigten die Ergebnisse der 6 Modelle eine sehr vergleichbare Leistung mit lediglich einer mittleren Abweichung um den Medianwert von 0,08 °C zwischen den einzelnen Modellen. Im größten untersuchten Einzugsgebiet – Donau bei Kienstock – wiesen Recurrent Neural Networks die höchste Modellgüte auf, was darauf hinweist, dass sie sich am besten eignen, wenn im Einzugsgebiet Prozesse mit langfristigen Abhängigkeiten ausschlaggebend sind. Die Wahl der Hyperparameter beeinflusste die Vorhersagefähigkeit der Modelle stark, was die Bedeutung der Hyperparameteroptimierung besonders hervorhebt.Die Ergebnisse dieser Studie fassen die Bedeutung unterschiedlicher Eingangsdaten, Modelle und Trainingscharakteristiken für die Modellierung von mittleren täglichen Fließgewässertemperaturen zusammen. Gleichzeitig dient diese Studie als Basis für die Entwicklung zukünftiger Modelle für eine regionale Fließgewässertemperaturvorhersage. Die getesteten Modelle stehen im open source R‑Paket wateRtemp allen AnwenderInnen der Forschungsgemeinschaft und der Praxis zur Verfügung.

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Experimental Evaluation of Computer Vision and Machine Learning-Based UAV Detection and Ranging

Drones ◽

10.3390/drones5020037 ◽

2021 ◽

Vol 5 (2) ◽

pp. 37

Author(s):

Bingsheng Wei ◽

Martin Barczyk

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Tracking System ◽

Ground Truth ◽

White Background ◽

Cascade Classifier ◽

Detection Algorithms ◽

Squared Error ◽

Test Conditions ◽

Video Feed

We consider the problem of vision-based detection and ranging of a target UAV using the video feed from a monocular camera onboard a pursuer UAV. Our previously published work in this area employed a cascade classifier algorithm to locate the target UAV, which was found to perform poorly in complex background scenes. We thus study the replacement of the cascade classifier algorithm with newer machine learning-based object detection algorithms. Five candidate algorithms are implemented and quantitatively tested in terms of their efficiency (measured as frames per second processing rate), accuracy (measured as the root mean squared error between ground truth and detected location), and consistency (measured as mean average precision) in a variety of flight patterns, backgrounds, and test conditions. Assigning relative weights of 20%, 40% and 40% to these three criteria, we find that when flying over a white background, the top three performers are YOLO v2 (76.73 out of 100), Faster RCNN v2 (63.65 out of 100), and Tiny YOLO (59.50 out of 100), while over a realistic background, the top three performers are Faster RCNN v2 (54.35 out of 100, SSD MobileNet v1 (51.68 out of 100) and SSD Inception v2 (50.72 out of 100), leading us to recommend Faster RCNN v2 as the recommended solution. We then provide a roadmap for further work in integrating the object detector into our vision-based UAV tracking system.

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A new generic method to improve machine learning applications in official statistics

Statistical Journal of the IAOS ◽

10.3233/sji-210885 ◽

2021 ◽

pp. 1-16

Author(s):

Kevin Kloos

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Statistical Properties ◽

Machine Learning Algorithms ◽

Official Statistics ◽

Academic Literature ◽

Misclassification Bias ◽

Squared Error ◽

Machine Learning Applications ◽

Applications Of Machine Learning

The use of machine learning algorithms at national statistical institutes has increased significantly over the past few years. Applications range from new imputation schemes to new statistical output based entirely on machine learning. The results are promising, but recent studies have shown that the use of machine learning in official statistics always introduces a bias, known as misclassification bias. Misclassification bias does not occur in traditional applications of machine learning and therefore it has received little attention in the academic literature. In earlier work, we have collected existing methods that are able to correct misclassification bias. We have compared their statistical properties, including bias, variance and mean squared error. In this paper, we present a new generic method to correct misclassification bias for time series and we derive its statistical properties. Moreover, we show numerically that it has a lower mean squared error than the existing alternatives in a wide variety of settings. We believe that our new method may improve machine learning applications in official statistics and we aspire that our work will stimulate further methodological research in this area.

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Comparison of ANFIS and FAHP-FGP methods for supplier selection

Kybernetes ◽

10.1108/k-09-2014-0195 ◽

2016 ◽

Vol 45 (3) ◽

pp. 474-489 ◽

Cited By ~ 6

Author(s):

Moloud sadat Asgari ◽

Abbas Abbasi ◽

Moslem Alimohamadlou

Keyword(s):

Performance Measures ◽

Supplier Selection ◽

Mean Squared Error ◽

Selection Process ◽

Evaluation Criteria ◽

Absolute Error ◽

Global Market ◽

Content Type ◽

Squared Error ◽

Fuzzy Delphi

Purpose – In the contemporary global market, supplier selection represents a crucial process for enhancing firms’ competitiveness. This is a multi-criteria decision-making problem that involves consideration of multiple criteria. Therefore this requires reliable methods to select the best suppliers. The purpose of this paper is to examine and propose appropriate method for selecting suppliers. Design/methodology/approach – ANFIS and fuzzy analytic hierarchy process-fuzzy goal programming (FAHP-FGP) are new methods for evaluating and selecting the best suppliers. These methods are used in this study for evaluating suppliers of dairy industries and the results obtained from methods are compared by performance measures such as Mean Squared Error, Root Mean Squared Error, Normalized Root Men Squared Error, Mean Absolute Error, Normalized Root Men Squared Error, Minimum Absolute Error and R2. Findings – The results indicate that the ANFIS method provides better performance compared to the FAHP-FGP method in terms of the selected suppliers scoring higher in all the performance measures. Practical implications – The proposed method could help companies select the best supplier, by avoiding the influence of personal judgment. Originality/value – This study uses the well-structured method of the fuzzy Delphi in order to determine the supplier evaluation criteria as well as the most recent ANFIS and FAHP-FGP methods for supplier selection. In addition, unlike most other studies, it performs the selection process among all available suppliers.

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Linear Regression Algorithm in Machine Learning through MATLAB

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39410 ◽

2021 ◽

Vol 9 (12) ◽

pp. 989-995

Author(s):

Kalva Sindhu Priya

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Linear Regression ◽

Mean Squared Error ◽

Critical Part ◽

Squared Error ◽

Master Level ◽

Predicted Model ◽

The Given

Abstract: In the present scenario, it is quite aware that almost every field is moving into machine based automation right from fundamentals to master level systems. Among them, Machine Learning (ML) is one of the important tool which is most similar to Artificial Intelligence (AI) by allowing some well known data or past experience in order to improve automatically or estimate the behavior or status of the given data through various algorithms. Modeling a system or data through Machine Learning is important and advantageous as it helps in the development of later and newer versions. Today most of the information technology giants such as Facebook, Uber, Google maps made Machine learning as a critical part of their ongoing operations for the better view of users. In this paper, various available algorithms in ML is given briefly and out of all the existing different algorithms, Linear Regression algorithm is used to predict a new set of values by taking older data as reference. However, a detailed predicted model is discussed clearly by building a code with the help of Machine Learning and Deep Learning tool in MATLAB/ SIMULINK. Keywords: Machine Learning (ML), Linear Regression algorithm, Curve fitting, Root Mean Squared Error

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