Logistic Regression Model Optimization and Case Analysis

Zou, Xiaonan; Hu, Yong; Tian, Zhewen; Shen, Kaiyuan

doi:10.1109/iccsnt47585.2019.8962457

Cited by 177 publications

(63 citation statements)

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“…The first ML technique used was LR, a transformed version of linear regression using the logic function, which was useful to model the probability of an event given other variables, namely, the probability of belonging to a group based on predicted probabilities from 0 to 1, which is considered the standard classification method for binary problems [ 37 ]. The model inputs real values that are multiplied by a weight and the sum is entered to the logit function Equation (2), to obtain the probability of belonging to one or another group based on the function of the threshold value [ 38 , 39 , 40 ], where z is the linear sum

plus

, and so on up to

times

, where the Xs are the independent variables of interest,

the constant term, and

(slopes) representing the unknown parameters.

…”

Section: Methodsmentioning

confidence: 99%

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

et al. 2021

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Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

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plus

, and so on up to

times

, where the Xs are the independent variables of interest,

the constant term, and

(slopes) representing the unknown parameters.

…”

Section: Methodsmentioning

confidence: 99%

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Logistic regression menentukan hubungan antara output yang berupa binary classification dengan independent variables menggunakan probabilitas dengan cara memprediksi nilai untuk dependant variable (Fujii et al, 2015;Rushin et al, 2017;Zou et al, 2019). Bentuk matematika dari model logistic regression ditunjukkan pada Pers.…”

Section: Logistic Regressionunclassified

Perbandingan Model Logistic Regression dan Artificial Neural Network pada Prediksi Pembatalan Hotel

Putra

Azhar

2021

JISKa

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Prediction for canceled booking hotels is an important part of hotel revenue management systems in the modern era. Because the predicted result can be used for the optimization of hotel performance. The application of machine learning will be very helpful for predicting canceled booking hotels because machine learning can process complex data. In this research, the proposed methods are Artificial Neural Network (ANN) and Logistic Regression. Later it will be done five times experiments with hyperparameter tuning to see which method is the most optimal to do prediction canceled booking hotel. From five times experiments, experiments number five (logistic regression with GridSearchCV) is the most optimal for predicting canceled booking hotels, with 79.77% accuracy, 85.86% precision, and 55.07% recall.

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“…Logistic Regression Logistic regression is a supervised learning algorithm for predicting the likelihood of a target variable. In a two-class problem, the target or dependent variable is dichotomous, which implies there would be just two potential classes [6]. The logistic function produces output between 0 and 1.…”

Section: K-nearest Neighboring (Knn)mentioning

confidence: 99%

COVID-19 County Level Severity Classification with Imbalanced Dataset: A NearMiss Under-sampling Approach

Oladunni

Tossou

Haile

et al. 2021

Preprint

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COVID-19 pandemic that broke out in the late 2019 has spread across the globe. The disease has infected millions of people. Thousands of lives have been lost. The momentum of the disease has been slowed by the introduction of vaccine. However, some countries are still recording high number of casualties. The focus of this work is to design, develop and evaluate a machine learning county level COVID-19 severity classifier. The proposed model will predict severity of the disease in a county into low, moderate, or high. Policy makers will find the work useful in the distribution of vaccines. Four learning algorithms (two ensembles and two non-ensembles) were trained and evaluated. Class imbalance was addressed using NearMiss under-sampling of the majority classes. The result of our experiment shows that the ensemble models outperformed the non-ensemble models by a considerable margin.

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Logistic Regression Model Optimization and Case Analysis

Cited by 177 publications

References 0 publications

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Perbandingan Model Logistic Regression dan Artificial Neural Network pada Prediksi Pembatalan Hotel

COVID-19 County Level Severity Classification with Imbalanced Dataset: A NearMiss Under-sampling Approach

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