Article

A Unified Framework for Survival Prediction: Combining Machine Learning Feature Selection with Traditional Survival Analysis in Heart Failure and METABRIC Breast Cancer

2026-01-29

Abstract excerpt

<h4>Background: </h4> The clinical adoption of machine learning (ML) in survival analysis is often hindered by the "black box" nature of complex algorithms. This study presents a unified and clinically grounded framework that integrates ML-based feature selection with traditional survival analysis to bridge the gap between algorithmic predictive power and routine clinical interpretation. <h4>Methods:</h4> We emplo...

Topics

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

Identifiers and source

Literature Corpus work: cec70052-c061-5e41-9ca3-0f95b2f6f9b0
DOI: 10.20944/preprints202601.2325.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A Unified Framework for Survival Prediction: Combining Machine Learning Feature Selection with Traditional Survival Analysis in Heart Failure and METABRIC Breast CancerDOI 10.20944/preprints202601.2325.v1Read

Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and DatabasesSemantic proximity Read

Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.Semantic proximity Read

A software package for simple and rigorous survival machine learning analysis in biomedical researchSemantic proximity Read

SurvivalML: an integrative platform for the discovery and exploration of prognostic models in multi-center cancer cohortsSemantic proximity Read

Development and Validation of a parsimonious AI-Based Risk Score for Mortality in Heart Failure: A UK cohort studySemantic proximity Read

Performance of Risk Models to Predict Mortality Risk for Patients with Heart Failure: Evaluation in an Integrated Health SystemSemantic proximity Read

Heart Failure Prediction & Risk Stratification using Machine LearningSemantic proximity Read

Development of an ensemble machine learning prognostic model to predict 60-day risk of major adverse cardiac events in adults with chest painSemantic proximity Read

Machine learning-based survival analysis and risk stratification for patients with cholangiocarcinomaSemantic proximity Read

Heart Failure Prediction & Risk Stratification using Machine LearningSemantic proximity Read

An Ensemble-Base Machine Learning Approach to Predict 2- and 10-Year Breast CancerSemantic proximity Read

Comprehensive, Transparent, and Fair Machine Learning Models for Hypertension Risk Prediction: Benchmarking With Framingham, External Validation, Individual-Level Analysis, and Equitable Clinical UtilitySemantic proximity Read