Decoding the Heart: Trustworthy AI for Predicting Sudden Cardiac Death

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Author: Denis Avetisyan

A new approach combines machine learning with logic-based explanations to build confidence in predicting life-threatening events in patients with Chagas disease.

Researchers develop a method for generating correct and interpretable explanations for sudden cardiac death prediction using XGBoost and first-order logic.

Despite advances in artificial intelligence, the ‘black box’ nature of many machine learning models hinders their clinical adoption, particularly in high-stakes scenarios like predicting sudden cardiac death. This is especially true for Chagas cardiomyopathy, where identifying at-risk patients remains a significant challenge. In ‘Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy’, we present a logic-based explainability method that provides both high predictive accuracy – exceeding 95% – and 100% explanation fidelity, offering a demonstrably trustworthy approach to risk stratification. Could this increased transparency facilitate wider implementation of AI-driven tools, especially in resource-limited regions where early detection is critical?

Decoding Silence: Predicting Cardiac Crisis

Chagas disease, a parasitic infection prevalent throughout Latin America, represents a considerable and often overlooked public health crisis. Transmitted primarily by triatomine bugs – known as “kissing bugs” – the disease can remain asymptomatic for decades, silently causing progressive damage to the heart and digestive system. Ultimately, a significant proportion of those infected develop chronic cardiac complications, frequently leading to debilitating heart failure, life-threatening arrhythmias, and, tragically, sudden cardiac death. The insidious nature of the disease, combined with limited access to diagnosis and treatment in many affected regions, contributes to its high morbidity and mortality rates, making it a leading cause of preventable death across the continent and an emerging concern for global health as migration patterns shift.

The timely identification of individuals susceptible to Sudden Cardiac Death presents a considerable challenge, despite its critical importance for effective intervention. Conventional diagnostic tools, such as electrocardiograms and echocardiograms, often lack the sensitivity to detect subtle cardiac abnormalities in the early stages of disease, leading to a high rate of false negatives. Furthermore, these methods frequently require specialized expertise for accurate interpretation, and their accessibility can be limited in resource-constrained settings. Consequently, a significant proportion of at-risk patients remain undiagnosed until a life-threatening event occurs, highlighting the urgent need for more proactive and reliable screening strategies that can overcome the limitations of current practices and enable preventative care.

While artificial intelligence demonstrates considerable potential in predicting sudden cardiac death, a significant barrier to widespread clinical adoption lies in its inherent lack of transparency – often described as a ‘black box’ phenomenon. These complex algorithms, though capable of identifying subtle patterns indicative of risk, frequently offer little insight into how a particular prediction was reached. This opaqueness fuels skepticism amongst clinicians, who require understandable rationales to confidently integrate AI-driven assessments into patient care. Without the ability to scrutinize the decision-making process, verifying the accuracy and identifying potential biases within the AI becomes difficult, hindering trust and limiting its practical application despite its predictive power. Consequently, research is increasingly focused on developing ‘explainable AI’ (XAI) techniques that can illuminate the factors driving these predictions, fostering confidence and ultimately improving patient outcomes.

Illuminating the Algorithm: The Promise of Explainable AI

Explainable AI (XAI) is a set of methods designed to reveal the internal logic of artificial intelligence models, moving beyond the “black box” approach common in many machine learning applications. This is achieved by providing human-understandable rationales for AI predictions, allowing stakeholders to assess the model’s reasoning process. The primary goal of XAI is to build trust and facilitate appropriate usage of AI systems, particularly in high-stakes domains where understanding why a prediction was made is as important as the prediction itself. Increased transparency allows for the identification of potential biases, errors, or unintended consequences within the model, and enables clinicians to validate AI-driven insights against their own clinical knowledge and experience.

XGBoost, a gradient boosting machine learning algorithm, has shown substantial predictive capability in cardiac risk assessment. Evaluations using patient datasets report an Area Under the Receiver Operating Characteristic Curve (AUC) of 95.00%, indicating a high degree of discrimination between patients with and without cardiac risk. Furthermore, the model achieves a Recall score of 95.00%, representing the proportion of actual positive cases correctly identified, demonstrating a strong ability to minimize false negatives in risk prediction. These metrics suggest XGBoost can effectively leverage patient data to identify individuals at high risk of cardiac events.

While Local Interpretable Model-agnostic Explanations (LIME) and Anchors are frequently employed for explaining predictions from complex machine learning models, these methods do not provide formal guarantees regarding the fidelity of their explanations. Specifically, LIME approximates the model locally with a simpler, interpretable model, but the resulting explanation may not accurately reflect the original model’s behavior, particularly outside the immediate vicinity of the prediction. Anchors identify rule-based conditions sufficient for a specific prediction; however, the identified anchor may not be the only condition leading to that outcome, or it may be unstable, changing with slight variations in input data. Consequently, clinicians relying on explanations generated by LIME or Anchors should exercise caution, as these explanations may be incomplete or potentially misleading, impacting clinical decision-making.

Formalizing Trust: Logic-Based Explanations

Logic-Based Abductive Explanations utilize First-Order Logic (FOL) and Linear Real Arithmetic (LRA) to construct explanations for model predictions with a formal correctness guarantee. This method translates the XGBoost model and dataset features into FOL and LRA statements, allowing for the formulation of explanations as logical proofs. Specifically, the approach identifies minimal sets of conditions, expressed as FOL/LRA formulas, that logically imply the model’s output given the input instance. The use of formal logic enables rigorous verification of explanation validity, ensuring that the generated explanations are not merely approximations but are demonstrably correct derivations based on the model and data. This contrasts with post-hoc explanation methods that rely on approximations and may lack a formal guarantee of correctness.

The explanation generation process utilizes data directly from the input Dataset to construct justifications for each XGBoost model prediction. This is achieved by identifying minimal sets of conditions – specific feature values and their relationships – that, when considered in conjunction, logically support the model’s output. The identified conditions are not arbitrary; they represent a subset of the data that, through logical inference, validates the prediction made by the XGBoost model. Minimality is enforced to ensure explanations are concise and focus on the most salient factors driving the prediction, rather than including extraneous information. These conditions are derived using First-Order Logic and Linear Real Arithmetic, providing a formal and verifiable basis for the explanations.

Evaluation of the generated explanations centers on two primary metrics: Feature Importance and Fidelity. Feature Importance is assessed by aligning explanation features with the inherent feature importance scores calculated by the XGBoost model itself, ensuring explanations highlight drivers already identified by the model. Fidelity, measured as the percentage of instances where the explanation accurately predicts the model’s output, was achieved at 100% in this study, indicating a strong correlation between the explanation and the model’s decision-making process. This rigorous evaluation confirms that the generated explanations are not only accurate representations of the model’s logic but also clinically relevant due to their grounding in both model-derived importance and predictive accuracy.

Explanation Size, a quantifiable metric within our logic-based explanation framework, represents a critical trade-off between explanation accuracy and clinical applicability. While striving for high fidelity-and achieving 100% fidelity in generated explanations within this study-minimizing the number of conditions comprising an explanation is equally important for practical use. Larger explanations, while potentially more comprehensive, can reduce clinician trust and hinder effective integration into clinical workflows. Therefore, our methodology prioritizes identifying minimal sets of conditions derived from the dataset that fully justify the XGBoost model’s predictions, ensuring both correctness and usability.

Beyond Prediction: Towards Trustworthy AI in Medicine

Recent research highlights a crucial intersection between artificial intelligence and clinical practice: the ability to generate explanations that are both accurate and easily understood. The work demonstrates that complex AI decision-making processes can be distilled into concise, human-interpretable rationales, fostering greater trust among healthcare professionals. This transparency is not merely about revealing how an AI arrived at a conclusion, but ensuring the explanation faithfully reflects the underlying reasoning – a critical factor in responsible AI deployment. By providing clinicians with clear, verifiable justifications, these explanations empower informed decision-making, allowing practitioners to critically evaluate AI suggestions and integrate them effectively into patient care. Ultimately, this approach moves beyond ‘black box’ AI, enabling a collaborative partnership between human expertise and artificial intelligence for improved healthcare outcomes.

A significant barrier to the adoption of artificial intelligence in clinical settings is the inherent ‘black box’ nature of many algorithms. This work directly addresses this challenge by establishing a mathematically rigorous framework for explainable AI. Instead of simply presenting predictions, the system provides justifications rooted in formal logic and quantifiable metrics, allowing clinicians to understand why a particular recommendation was made. This isn’t merely about providing post-hoc rationalizations; the explanations are integral to the model’s design, ensuring they are faithful representations of the underlying reasoning process. By grounding explanations in mathematical principles – such as feature \ attribution \ scores and counterfactual \ analysis – the system fosters trust and accountability, ultimately enabling more informed and confident clinical decision-making.

The capacity for artificial intelligence to enhance clinical practice is increasingly linked to its ability to not only predict outcomes, but to justify those predictions. Recent studies demonstrate that when AI models are paired with verifiable explanations – detailing why a specific conclusion was reached – performance improves significantly, leading to earlier and more effective interventions. This isn’t merely about transparency; the process of generating explanations forces models to focus on salient, clinically relevant features, reducing reliance on spurious correlations. Consequently, clinicians can more confidently integrate AI insights into their decision-making, potentially identifying at-risk patients sooner and tailoring treatments with greater precision. This feedback loop – explanation driving improved performance and increased clinical trust – ultimately translates to better patient outcomes and a more proactive approach to healthcare.

The pursuit of reliable explanations in complex systems demands a willingness to challenge established boundaries. This research, focused on predicting sudden cardiac death, embodies that spirit. It doesn’t simply accept the ‘black box’ output of machine learning; instead, it dissects the model’s logic, demanding correctness guarantees. Ada Lovelace observed, “That brain of mine is something more than merely mortal; as time will show.” The work mirrors this sentiment – an attempt to push beyond the limitations of current understanding, employing first-order logic not just as a descriptive tool, but as a method for verifying the reasoning behind predictions. The insistence on logic-based explanations isn’t about creating simpler models, but about ensuring the explanations themselves are beyond reproach, revealing the underlying mechanisms with verifiable accuracy. It’s a process of intellectual reverse-engineering, akin to Lovelace’s vision of a machine capable of more than calculation.

Beyond the Black Box

The pursuit of reliable explanations in predictive modeling, as demonstrated with Chagas cardiomyopathy and sudden cardiac death, inevitably exposes the fragility of ‘interpretability’ itself. Generating logic-based explanations offers a compelling corrective to post-hoc rationalizations, but it merely shifts the burden of verification. The system now guarantees the form of the explanation, not necessarily its clinical relevance-a subtle, yet crucial, distinction. Future work must address how to rigorously validate the usefulness of these logically sound, yet potentially spurious, correlations.

One anticipates a necessary divergence toward methods that do not simply explain existing models, but actively constrain their learning process. If a model cannot learn patterns without producing verifiable, first-order logic statements, the resultant loss in predictive power becomes a price worth paying. The current emphasis on explanation feels akin to building increasingly detailed maps of a phantom, rather than dismantling the illusion itself.

Ultimately, the true test will not be whether explanations are correct, but whether they are falsifiable – and whether clinicians are willing to accept the inevitable instances where a logically sound explanation proves clinically meaningless. The system’s strength lies in transparent failure, not in perfect prediction. It is a humble beginning, certainly, but a necessary one if the field hopes to move beyond conjuring intelligence and toward genuinely understanding it.

Original article: https://arxiv.org/pdf/2602.22288.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-27 22:54