Key result
Genetic programming classifier predicts worsening heart failure with ~96% accuracy, beating standard machine learning models.
Why the study?
AI models for predicting worsening heart failure are often considered unreliable black boxes by physicians due to their lack of explainability.
Does a Genetic Programming-based classifier improve the prediction of Worsening Heart Failure compared to common machine learning algorithms in heart failure patients?
Observational (n=519)
No
Does a Genetic Programming-based classifier improve the prediction of Worsening Heart Failure compared to common machine learning algorithms in heart failure patients?
An explainable genetic programming-based model accurately predicts worsening heart failure, offering a transparent tool for early clinical intervention.
Answers are grounded in this paper and cite their sources.
May support early worsening heart failure detection in cohorts; extends genetic programming models but leaves prospective validation open.
Heart Failure (HF) poses a challenge for our health systems, and early detection of Worsening HF (WHF), defined as a deterioration in symptoms and clinical and instrumental signs of HF, is vital to improving prognosis. Predicting WHF in a phase that is currently undiagnosable by physicians would enable prompt treatment of such events in patients at a higher risk of WHF. Although the role of Artificial Intelligence in cardiovascular diseases is becoming part of clinical practice, especially for diagnostic and prognostic purposes, its usage is often considered not completely reliable due to the incapacity of these models to provide a valid explanation about their output results. Physicians are often reluctant to make decisions based on unjustified results and see these models as black boxes. This study aims to develop a novel diagnostic model capable of predicting WHF while also providing an easy interpretation of the outcomes. We propose a threshold-based binary classifier built on a mathematical model derived from the Genetic Programming approach. This model clearly indicates that WHF is closely linked to creatinine, sPAP, and CAD, even though the relationship of these variables and WHF is almost complex. However, the proposed mathematical model allows for providing a 3D graphical representation, which medical staff can use to better understand the clinical situation of patients. Experiments conducted using retrospectively collected data from 519 patients treated at the HF Clinic of the University Hospital of Salerno have demonstrated the effectiveness of our model, surpassing the most commonly used machine learning algorithms. Indeed, the proposed GP-based classifier achieved a 96% average score for all considered evaluation metrics and fully supported the controls of medical staff. Our solution has the potential to impact clinical practice for HF by identifying patients at high risk of WHF and facilitating more rapid diagnosis, targeted treatment, and a reduction in hospitalizations.
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Visco et al. (2024) conducted an observational in Worsening Heart Failure (n=519). Genetic Programming-based binary classifier vs. Commonly used machine learning algorithms was evaluated on Prediction of Worsening Heart Failure (average score across evaluation metrics). A Genetic Programming-based binary classifier predicted worsening heart failure with a 96% average score across evaluation metrics, surpassing commonly used machine learning algorithms.
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