Heart failure survival prediction using novel transfer learning based probabilistic featuresShow others and affiliations
2024 (English)In: PeerJ Computer Science, E-ISSN 2376-5992, Vol. 10, article id e1894Article in journal (Refereed) Published
Abstract [en]
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 crossvalidation 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.
Place, publisher, year, edition, pages
PEERJ INC , 2024. Vol. 10, article id e1894
Keywords [en]
Transfer learning, Machine learning, Heart failure, Feature engineering
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-66353DOI: 10.7717/peerj-cs.1894ISI: 001182217200001Scopus ID: 2-s2.0-85190872607OAI: oai:DiVA.org:mdh-66353DiVA, id: diva2:1848338
2024-04-032024-04-032024-05-08Bibliographically approved