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Updated: Dec 29, 2025
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction
Davide Chicco1, Giuseppe Jurman2
1Krembil Research Institute, Toronto, Ontario, Canada. davidechicco@davidechicco.it.
Insights
Machine learning models can predict heart failure survival using only serum creatinine and ejection fraction. These two factors alone provide more accurate predictions than extensive patient data, aiding clinical decisions.
Area of Science:
- Cardiology
- Biostatistics
- Machine Learning
Background:
- Cardiovascular diseases, including heart failure (HF), cause millions of deaths annually.
- Electronic medical records contain valuable data for analyzing HF patient outcomes.
- Machine learning can identify survival patterns and key risk factors from patient data.
Purpose of the Study:
- To predict heart failure patient survival using machine learning.
- To identify crucial risk factors for heart failure from patient medical records.
- To compare machine learning feature ranking with traditional biostatistics.
Main Methods:
- Analysis of a 2015 heart failure patient dataset (299 patients).
- Application of machine learning classifiers for survival prediction and feature ranking.
- Comparison of machine learning feature importance with biostatistics tests.
Main Results:
- Serum creatinine and ejection fraction were identified as the most relevant features by both methods.
- Two-feature models using only these factors achieved higher prediction accuracy than using the full dataset.
- These two features were sufficient for predicting patient survival, even with follow-up month data.
Conclusions:
- Serum creatinine and ejection fraction are sufficient for accurate heart failure survival prediction.
- This finding can support clinical practice by focusing physician attention on key indicators.
- Simplified prediction models based on these two factors can improve patient outcome assessment.
Background:
Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records.
Methods:
In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone.
Results:
Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival.
Conclusions:
This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.
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