Frontiers

A comparative study of risk factors in predictive models for cognitive dysfunction in patients with leukoaraiosis based on machine learning algorithms

Objective: To explore the risk factors of cognitive dysfunction in patients with leukoaraiosis (LA) and to construct a predictive model using machine learning. Methods: A total of 273 patients with LA ...
Scientific Research Publishing

Johnson, A. and Lee, M. (2022) Predictive Modeling for Cardiovascular Risk Assessment Using Logistic Regression and Deep Learning. Journal of Biomedical Informatics, 130 ...

ABSTRACT: Heart disease remains one of the leading causes of mortality worldwide, accounting for millions of deaths annually. Early detection of individuals at risk is essential for reducing ...
Scientific Research Publishing

Heart Disease Prediction: A Logistic Regression Approach ()

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...
ebm.bmj

Hidden risks of predictive models in healthcare

Joseph Alderman et al argue that predictive models in healthcare lack adequate oversight and regulation. They highlight the potential risks to patients and call for improved governance to ensure the ...
GitHub

SarahRoWo/Logistic_Regression_Python_MI_Paper

This set of notebooks enables the analysis of comorbidities associated with male infertility using structured EHR data. First, we identified nonoverlapping patients with male infertility and patients ...
IEEE

Predictive Modeling of Lung Cancer Detection Using Logistic Regression on Liquid Biopsy

Abstract: Lung cancer remains one of the most important issues related to cancer mortality worldwide due to the late diagnosis and the need for invasive biopsy procedures. There has emerged a new and ...
Frontiers

Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest

Objective: In this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP ...