Development and Validation of an Explainable Prediction Model to Assess the Risk of Coronary Artery Disease in Young and Middle-Aged Individuals

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Background: There is currently a lack of adequate risk assessment for coronary artery disease in the young and middle-aged population (ages 20-60). This cohort is characterized by limited symptom presentation, low utilization of medical facilities, and challenges in accessing healthcare services. Consequently, these individuals experience difficulties in early disease identification, rendering them susceptible to sudden cardiac death and premature mortality upon the manifestation of symptoms. Data from regular blood and urine tests, as well as questionnaires, are readily available and well-documented across diverse healthcare environments. Hypertension is a notable risk for coronary artery disease within this population. In light of these challenges, we present a risk assessment system for coronary heart disease specifically tailored for young and middle-aged individuals with hypertension, utilizing data derived from blood and urine examinations in conjunction with a brief questionnaire. Methods: The dataset was sourced from the National Health and Nutrition Examination Survey (NHANES) database, covering the years 2005-2019. Following three iterations of feature selection, we identified 26 pertinent features. Subsequently, we developed five predictive models to facilitate large-scale screening for coronary heart disease risk. To enhance the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) to evaluate the individual contributions of each feature. Results: We included 709 patients diagnosed with coronary artery disease and 6409 healthy individuals in our analysis. The results showed that LightGBM exhibited the highest performance (area under the curve (AUC) of 0.93). Conclusions: This study has the potential to facilitate the improved screening of patients with coronary artery disease; we have developed a risk assessment system that is freely accessible to the public: https://prediction-of-coronary-heart-disease-htn-young-adults.streamlit.app/.

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