A machine learning approach for Premature Coronary Artery Disease Diagnosis according to Different Ethnicities in Iran

• URL: http://arxiv.org/abs/2501.18893v1
• Date: Fri, 31 Jan 2025 05:20:56 GMT
• Title: A machine learning approach for Premature Coronary Artery Disease Diagnosis according to Different Ethnicities in Iran
• Authors: Mohamad Roshanzamir, Roohallah Alizadehsani, Ehsan Zarepur, Noushin Mohammadifard, Fatemeh Nouri, Mahdi Roshanzamir, Alireza Khosravi, Fereidoon Nouhi, Nizal Sarrafzadegan,
• Abstract summary: Premature coronary artery disease (PCAD) refers to the early onset of the disease, usually before the age of 55 for men and 65 for women. In this study, we tested the rank of ethnicity among the major risk factors of PCAD. Gender and age were the most significant predictors, with ethnicity being the third most important.
• Score: 0.9674145073701151
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• Abstract: Premature coronary artery disease (PCAD) refers to the early onset of the disease, usually before the age of 55 for men and 65 for women. Coronary Artery Disease (CAD) develops when coronary arteries, the major blood vessels supplying the heart with blood, oxygen, and nutrients, become clogged or diseased. This is often due to many risk factors, including lifestyle and cardiometabolic ones, but few studies were done on ethnicity as one of these risk factors, especially in PCAD. In this study, we tested the rank of ethnicity among the major risk factors of PCAD, including age, gender, body mass index (BMI), visceral obesity presented as waist circumference (WC), diabetes mellitus (DM), high blood pressure (HBP), high low-density lipoprotein cholesterol (LDL-C), and smoking in a large national sample of patients with PCAD from different ethnicities. All patients who met the age criteria underwent coronary angiography to confirm CAD diagnosis. The weight of ethnicity was compared to the other eight features using feature weighting algorithms in PCAD diagnosis. In addition, we conducted an experiment where we ran predictive models (classification algorithms) to predict PCAD. We compared the performance of these models under two conditions: we trained the classification algorithms, including or excluding ethnicity. This study analyzed various factors to determine their predictive power influencing PCAD prediction. Among these factors, gender and age were the most significant predictors, with ethnicity being the third most important. The results also showed that if ethnicity is used as one of the input risk factors for classification algorithms, it can improve their efficiency. Our results show that ethnicity ranks as an influential factor in predicting PCAD. Therefore, it needs to be addressed in the PCAD diagnostic and preventive measures.

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