Explainable machine learning model for assessing health status in patients with comorbid coronary heart disease and depression: Development and validation study

 Title: Explainable Machine Learning Model for Assessing Health Status in Patients with Comorbid Coronary Heart Disease and Depression

Introduction

Coronary heart disease (CHD) and depression often coexist, significantly impacting patient health outcomes. Traditional diagnostic methods face challenges in effectively assessing health status in such cases. Machine learning (ML) offers a promising solution, but the need for explainability remains crucial for clinical adoption. This blog explores the development and validation of an explainable ML model to assess patient health status in those with comorbid CHD and depression.

Understanding Explainable AI in Healthcare

Explainable artificial intelligence (XAI) enhances transparency in ML models, helping healthcare professionals interpret predictions. Unlike black-box models, explainable ML provides insights into key risk factors, enabling better clinical decision-making.

Model Development

The ML model was trained using patient data, including demographics, clinical symptoms, medical history, and lifestyle factors. Feature selection methods identified the most critical predictors, such as heart rate variability, medication adherence, and psychological stress levels. Decision trees, SHAP (Shapley Additive Explanations), and LIME (Local Interpretable Model-agnostic Explanations) techniques were incorporated to improve interpretability.

Validation and Performance

The model was validated using real-world patient data from medical databases. Performance metrics such as accuracy, sensitivity, and specificity were analyzed. The explainable model provided actionable insights into how different variables influenced predictions, enhancing clinician trust.

Clinical Implications

By offering personalized risk assessments, the explainable ML model aids in early intervention and tailored treatment plans. It bridges the gap between AI-driven healthcare and clinical expertise, ensuring both accuracy and interpretability.

Conclusion

The integration of explainable ML models in healthcare can revolutionize patient management, particularly for those with complex comorbid conditions like CHD and depression. Future research should focus on further refining these models and expanding their applications in diverse patient populations.

International Phenomenological Research Awards

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