A Machine Learning Approach for Chronic Heart Failure Diagnosis.
Dafni K Plati1, Evanthia E Tripoliti1, Aris Bechlioulis2
1Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, 45110 Ioannina, Greece.
Diagnostics (Basel, Switzerland)
|October 23, 2021
Summary
Machine learning (ML) accurately diagnoses chronic heart failure (HF) using clinical, echocardiogram, and lab data. Models achieved over 91% accuracy, demonstrating the effectiveness of ML in HF diagnostics.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
- Cardiology
Background:
- Chronic heart failure (HF) diagnosis poses clinical challenges.
- Machine learning (ML) offers potential for improving diagnostic accuracy.
Purpose of the Study:
- To evaluate ML approaches for diagnosing chronic heart failure (HF).
- To assess the diagnostic value of combining clinical, echocardiogram, and laboratory features.
- To investigate the incremental contribution of each feature category.
Main Methods:
- Developed ML models using feature selection, class imbalance handling, and classification.
- Utilized a dataset of 422 subjects.
- Simulated clinical diagnostic procedures by testing various feature combinations.
Main Results:
- Achieved high diagnostic accuracy (91.23%) for HF when using all feature categories.
- Demonstrated high sensitivity (93.83%) and specificity (89.62%).
- Maintained strong performance even when using single feature types.
Conclusions:
- ML models are effective for diagnosing chronic heart failure (HF).
- Combining diverse data types (clinical, echocardiogram, laboratory) enhances diagnostic performance.
- Individual feature types also provide valuable diagnostic information.
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