Article

Impact of Class Imbalance on Binary Outcome Predictions: A Comparative Study of Thresholding Strategies with LASSO

2026-05-21

Abstract excerpt

<title>Abstract</title> <p>Class imbalance is a pervasive challenge in health outcome prediction, where clinically critical events, such as malignant diagnoses, occur far less frequently than non-events. Traditional approaches often rely on resampling or synthetic data generation to mitigate imbalance, which can alter the original data distribution and reduce interpretability. This study demonstrates that careful...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 28db1524-f264-5a43-bdf6-39d0dca304d7
DOI: 10.21203/rs.3.rs-8941439/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Impact of Class Imbalance on Binary Outcome Predictions: A Comparative Study of Thresholding Strategies with LASSODOI 10.21203/rs.3.rs-8941439/v1Read

Estimating misclassification error with small samples via bootstrap cross-validation.Semantic proximity Read

Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity predictionSemantic proximity Read

An Empirical study on the class imbalance handling techniques for different diseasesSemantic proximity Read

Sensitivity-Constrained Evolutionary Feature Selection for Imbalanced Medical Classification: A Case Study on Rotator Cuff Tear Surgery PredictionSemantic proximity Read

“A Population-Specific Breast Cancer Risk Prediction Model for Indian Women (A Pilot Study): Advancing Beyond Traditional Assessment Tools”Semantic proximity Read

Empirical Sample Size Determination for Popular Classification Algorithms in Clinical ResearchSemantic proximity Read

Enhanced machine learning and hybrid ensemble approaches for coronary heart disease predictionSemantic proximity Read

Cervical Cancer Prediction based on Imbalanced Data using Machine Learning AlgorithmsSemantic proximity Read

The role of balanced training and testing data sets for binary classifiers in bioinformatics.Semantic proximity Read

Optimizing Machine Learning for Diabetes Detection: Addressing Class Imbalance with SMOTE and Random Forest Ensemble LearningSemantic proximity Read

Improving classification on imbalanced genomic data via KDE–based synthetic samplingSemantic proximity Read

An Improved Decision Tree Model with Hybrid Training Subset Selection for Robust Cardiovascular Disease PredictionSemantic proximity Read