AIMC Topic: Regression Analysis
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Do Machine Learning Approaches Perform Better Than Regression Models in Mapping Studies? A Systematic Review.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Feb 6, 2025
OBJECTIVES: To identify how machine learning (ML) approaches were implemented in mapping studies and to determine the extent to which ML improved performance compared with regression models (RMs).
Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma.
Annals of surgical oncology
Jan 28, 2025
BACKGROUND: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).
On spectral bias reduction of multi-scale neural networks for regression problems.
Neural networks : the official journal of the International Neural Network Society
Jan 21, 2025
In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep neural networks (MscaleDNN) (Cai and Xu, 2019; Liu et al., 2020), which is designed to reduce the spec...
Reducing bias in source-free unsupervised domain adaptation for regression.
Neural networks : the official journal of the International Neural Network Society
Jan 17, 2025
Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by leveraging a pre-trained source model without access to source data. While existing studies attempt to tr...
Nonlinear feature selection for support vector quantile regression.
Neural networks : the official journal of the International Neural Network Society
Jan 13, 2025
This paper discusses the nuanced domain of nonlinear feature selection in heterogeneous systems. To address this challenge, we present a sparsity-driven methodology, namely nonlinear feature selection for support vector quantile regression (NFS-SVQR)...
Mortality prediction after major surgery in a mixed population through machine learning: a multi-objective symbolic regression approach.
Anaesthesia
Jan 8, 2025
INTRODUCTION: Understanding 1-year mortality following major surgery offers valuable insights into patient outcomes and the quality of peri-operative care. Few models exist that predict 1-year mortality accurately. This study aimed to develop a predi...
Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.
International dental journal
Jan 5, 2025
OBJECTIVE: This study aimed to predict long-term growth-related changes in skeletal and dental relationships within the craniofacial complex using machine learning (ML) models.
Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset.
PloS one
Dec 31, 2024
Accurate forecasting of claim frequency in automobile insurance is essential for insurers to assess risks effectively and establish appropriate pricing policies. Traditional methods typically rely on a Poisson distribution for modeling claim counts; ...
Alternative assessment of machine learning to polynomial regression in response surface methodology for predicting decolorization efficiency in textile wastewater treatment.
Chemosphere
Dec 20, 2024
This study investigated the potential of machine learning (ML) as a substitute for polynomial regression in conventional response surface methodology (RSM) for decolorizing textile wastewater via a UV/HO process. While polynomial regression offers li...
Tensor Coupled Learning of Incomplete Longitudinal Features and Labels for Clinical Score Regression.
IEEE transactions on pattern analysis and machine intelligence
Dec 4, 2024
Longitudinal data with incomplete entries pose a significant challenge for clinical score regression over multiple time points. Although many methods primarily estimate longitudinal scores with complete baseline features (i.e., features collected at ...
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