Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam

doi:10.1063/1.4932497

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…High percentage of having mild stress were indicated by respondents aged 20 to 39 years old (29.8%), unmarried (26.8%), with less than five children (25.1%), with heavy workload (26.4), and with administrative responsibility (26.8%). , , X i , p , X i that has k -1 non-overlapping logit model [6].…”

Section: Data and Variablementioning

confidence: 99%

Multinomial logistic regression modelling of stress level among secondary school teachers in Kubang Pasu District, Kedah

Mohamad¹,

Ali²,

Noor³

et al. 2016

AIP Conference Proceedings

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Section: Data and Variablementioning

confidence: 99%

Multinomial logistic regression modelling of stress level among secondary school teachers in Kubang Pasu District, Kedah

Mohamad¹,

Ali²,

Noor³

et al. 2016

AIP Conference Proceedings

Self Cite

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“…As mentioned above, a number of studies have used multinomial logistic regression in recent years to analyze the impact of a set of variables on a multi‐category nominal dependent variable (Aziz et al, 2016; Ghazali et al, 2015; Grilo et al, 2017; Mohamad et al, 2016). With this in mind, we used this statistical test to analyze our data.…”

Section: Resultsmentioning

confidence: 99%

The effects of the socio‐demographic factors on judgement building in arbitration

Jafary,

Carrière

2024

Industrial Relations Journal

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This study examines how the socio‐demographic characteristics of arbitrators and of plaintiffs affect arbitrators' judgement bases for arbitration decisions. Two research questions are tested quantitatively based on a data set of arbitration decisions in the Canadian university sector collected from the website of the Canadian Legal Information Institute. We created two models of independent variables related to the socio‐demographic characteristics of arbitrators and plaintiffs. Multinomial logistic regression is used to examine the possible impacts of these variables on the justifications used by arbitrators to explain their decisions. The results indicate that both models significantly influence how arbitrators justify their arbitral decisions. The following variables significantly contribute to the models: arbitrator's age, arbitrator's professional experience in management, plaintiff's gender, and support of the plaintiff by a collective entity (union or association). Young arbitrators are more likely to use “laws” and those who have professional experience in management tend to cite “evidence” to justify their arbitral decisions. Also, arbitrators are more likely to use “evidence” as their judgement basis for male plaintiffs who are supported by a collective entity. The details of these findings, limitations of the study, and future directions for research are further discussed.

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“…Therefore, it is an opportunity to apply machine learning algorithms to classify individual patients in medical practice, treat them, and control their future possible consequences. Using various machine learning prediction models, let the physicians and the health staff be able to extract the minimum necessary data to make a precise decision about people with normal and non-normal BMI (12).…”

Section: Discussionmentioning

confidence: 99%

Using machine learning method for classification body mass index of people for clinical decision

et al. 2022

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Introduction: Body mass index (BMI) is an acceptable method to measure overweight and obesity among the population. Objectives: The aim of this study was evaluating the application of machine learning algorithms for classifying body mass index for clinical purposes. Patients and Methods: In this descriptive study, we selected the dataset of 1316 people who selected randomly from all area of Ardabil city in Iran. Dataset included demographic and anthropometric data. Classification algorithms such as random forest (RF), Gaussian Naive Bayes (GNB), decision tree (DT), support vector machines (SVM), multi-layer perceptron (MLP), K-nearest neighbors (KNN) and logistic regression (LR) with 10-fold cross-validation were conducted to classify the data based on BMI. The performance of algorithms was evaluated with precision, recall, mean squared errors (MSE) and accuracy indices. All programing done by Python 3.7 in Jupyter Notebook. Results: According to the BMI, 603(45.8%) of all samples were normal and 713 (54.2%) were at-risk. The precision of RF, GNB, DT, SVM, MLP, KNN and LR for people at risk were 0.93, 0.86, 0.99, 0.82, 100, 0.82 and 0.99 respectively. Additionally, the accuracy of RF, GNB, DT, SVM, MLP, KNN and LR were 95%, 83%, 100%, 82%, 100%, 82% and 100 %. Conclusion: The comparison of the classifying algorithms showed that, the LR, MLP and DT had the higher accuracy than the other algorithms in detecting of people at-risk.

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Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

Cited by 3 publications

References 6 publications

Multinomial logistic regression modelling of stress level among secondary school teachers in Kubang Pasu District, Kedah

Multinomial logistic regression modelling of stress level among secondary school teachers in Kubang Pasu District, Kedah

The effects of the socio‐demographic factors on judgement building in arbitration

Using machine learning method for classification body mass index of people for clinical decision

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