AIMC Topic: Logistic Models

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Comparison of machine learning and logistic regression models for predicting emergence delirium in elderly patients: A prospective study.

International journal of medical informatics
OBJECTIVE: To compare the performance of machine learning and logistic regression algorithms in predicting emergence delirium (ED) in elderly patients.

Developing an interpretable machine learning model for screening depression in older adults with functional disability.

Journal of affective disorders
This study utilized data from the 2020 wave of the China Health and Retirement Longitudinal Study database, selecting 4322 participants aged 60 and above as the study sample. Important predictors of depression in older adults with functional disabili...

The combined multilayer perceptron and logistic regression (MLP-LR) method better predicted the spread of Hyphantria cunea (Lepidoptera: Erebidae).

Journal of economic entomology
Hyphantria cunea (Lepidoptera: Erebidae) is one of the pests that pose a serious threat to forest and agronomic crops in China. Its spread is influenced by various factors, including environmental factors and anthropogenic factors, and the available ...

Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.

Pharmacoepidemiology and drug safety
PURPOSE: The surge of treatments for COVID-19 in the second quarter of 2020 had a low prevalence of treatment and high outcome risk. Motivated by that, we conducted a simulation study comparing disease risk scores (DRS) and propensity scores (PS) usi...

Four Different Artificial Intelligence Models Logistic Regression to Enhance the Diagnostic Accuracy of Fecal Immunochemical Test in the Detection of Colorectal Carcinoma in a Screening Setting.

Anticancer research
BACKGROUND/AIM: This study aimed to evaluate the diagnostic accuracy (DA) of four artificial intelligence (AI) models compared to logistic regression (LR) in enhancing the performance of the fecal immunochemical test (FIT) for the detection of colore...

Temporal validation of machine learning models for pre-eclampsia prediction using routinely collected maternal characteristics: A validation study.

Computers in biology and medicine
BACKGROUND: Pre-eclampsia (PE) contributes to more than one-fourth of all maternal deaths and half a million newborn deaths worldwide every year. Early screening and interventions can reduce PE incidence and related complications. We aim to 1) tempor...

OptiStack classifier: optimized stacking framework with ensemble feature engineering for enhanced cardiovascular risk prediction.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]
BACKGROUND: Cardiovascular diseases (CVD) are a leading cause of morbidity and mortality globally, highlighting the urgent need for accurate risk prediction to improve early intervention and management. Traditional models have difficulty capturing th...

Comparative Analysis of Feature Extraction Methods and Machine Learning Models for Predicting Osteoporosis Prevalence.

Journal of medical systems
This study systematically examined the impact of three feature selection techniques (Boruta, Extreme gradient boosting (XGBoost), and Lasso) for optimizing four machine learning models (Random forest (RF), XGBoost, Logistic regression (LR), and Suppo...

Machine learning prediction model with shap interpretation for chronic bronchitis risk assessment based on heavy metal exposure: a nationally representative study.

BMC pulmonary medicine
BACKGROUND: Chronic bronchitis (CB), as a core precursor of Chronic Obstructive Pulmonary Disease (COPD), is crucial for global disease burden prevention and control. Although the association between heavy metal exposure and respiratory damage has be...

[Development and validation of risk assessment models for abnormal lung function in coal workers based on machine learning].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases
To analyze the factors influencing the lung function of coal miners, identify the optimal combination of indicators for evaluating lung function, develop a risk assessment model using machine learning, and offer personalized risk assessment for work...