Healthcare
AutoML and Ensemble Learning for Post-Heart Failure Survival Prediction Using MLJAR
- AutoML heart failure
- machine learning survival prediction
- AI cardiology risk stratification
- MLJAR AutoML research
- ensemble learning healthcare
- AUROC heart failure prediction
- automated machine learning medicine
- post heart failure mortality prediction
- clinical risk modeling AI
- state-of-the-art medical AI
MLJAR tools were used in the following publication.
Amalgamation of Auto Machine Learning and Ensemble Approaches to Achieve State-of-the-Art Post-Heart Failure Survival Predictions
Ali Haider Bangash, Ali Haider Shah, Arshiya Fatima, Saiqa Zehra, Syed Mohammad Mehmood Abbas, Hashir Fahim Khawaja, Muhammad Ashraf, Adil Baloch
Shifa College of Medicine, Shifa Tameer e Millat University, Islamabad, Pakistan
This study published in the American Heart Journal explores the use of MLJAR AutoML combined with ensemble approaches to predict post-heart failure mortality. Using a cohort of 299 heart failure patients, multiple classification algorithms were evaluated across different clinical scenarios. The best-performing ensemble models achieved AUROC up to 0.89, demonstrating that automated machine learning combined with ensemble methods can significantly improve survival prediction accuracy and risk stratification in heart failure management.
American Heart Journal • December 1, 2021
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