Heart failure survival prediction using novel transfer learning based probabilistic features

Heart failure is a complex cardiovascular condition characterized by the heart's inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims t...

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Bibliographic Details
Published in:PeerJ. Computer science 2024-03, Vol.10, p.e1894-e1894, Article e1894
Main Authors: Qadri, Azam Mehmood, Hashmi, Muhammad Shadab Alam, Raza, Ali, Zaidi, Syed Ali Jafar, Rehman, Atiq Ur
Format: Article
Language:English
Subjects:
Algorithms and Analysis of Algorithms
Analysis
Artificial Intelligence
Cardiac patients
Cardiovascular agents
Care and treatment
Computational Biology
Data Mining and Machine Learning
Data Science
Feature engineering
Heart failure
Hospital patients
Machine learning
Medical research
Medicine, Experimental
Patients
Transfer learning
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Summary:Heart failure is a complex cardiovascular condition characterized by the heart's inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques. We analyzed data from 299 hospitalized heart failure patients, addressing the issue of imbalanced data with the Synthetic Minority Oversampling (SMOTE) method. Additionally, we proposed a novel transfer learning-based feature engineering approach that generates a new probabilistic feature set from patient data using ensemble trees. Nine fine-tuned machine learning models are built and compared to evaluate performance in patient survival prediction. Our novel transfer learning mechanism applied to the random forest model outperformed other models and state-of-the-art studies, achieving a remarkable accuracy of 0.975. All models underwent evaluation using 10-fold cross-validation and tuning through hyperparameter optimization. The findings of this study have the potential to advance the field of cardiovascular medicine by providing more accurate and personalized prognostic assessments for individuals with heart failure.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.1894