Journal of Medical Internet Research

Beyond Comparing Machine Learning and Logistic Regression in Clinical Prediction Modelling: Shifting from Model Debate to Data Quality

The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...
IEEE

The Impact of Feature Scaling in Machine Learning: Effects on Regression and Classification Tasks

Abstract: This study addresses the lack of comprehensive evaluations of feature scaling by systematically assessing 12 techniques, including less common methods such as VAST and Pareto, in 14 machine ...
Frontiers

Development of an interpretable machine learning model for predicting sarcopenia in patients undergoing maintenance hemodialysis

Sarcopenia has a high incidence among patients undergoing maintenance hemodialysis (MHD), significantly increasing the risk of falls, fractures, and mortality. Traditional diagnostic methods, however, ...
Hosted on MSN

Logistic Regression Machine Learning Example ¦ Simply Explained

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...
Scientific Research Publishing

Machine Learning-Based Classification of Coronary Heart Disease: A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models ()

1 Department of Business Information System, Central Michigan University, Mount Pleasant, MI, USA. 2 Department of MPH, Central Michigan University, Mount Pleasant, MI, USA. 3 Department of ...
marktechpost

Google DeepMind Researchers Unlock the Potential of Decoding-Based Regression for Tabular and Density Estimation Tasks

Regression tasks, which involve predicting continuous numeric values, have traditionally relied on numeric heads such as Gaussian parameterizations or pointwise tensor projections. These traditional ...
IEEE

Predictive Analysis of Network-Based Attacks by Hybrid Machine Learning Algorithms Utilizing Bayesian Optimization, Logistic Regression, and Random Forest Algorithm

Abstract: These days, intrusion detection systems are one of the newest trends in society. These technologies serve as a defense against a variety of security breaches, the number of which has been ...