November 14, 2022Computers in Biology and Medicine56 citationsOpen Access

Using a machine learning-based risk prediction model to analyze the coronary artery calcification score and predict coronary heart disease and risk assessment

Structured PICO

Can machine learning models combining coronary artery calcification scores and clinical factors accurately predict the probability of coronary heart disease?

Population

Patients suspected of coronary heart disease (CHD) who underwent coronary artery computed tomography angiography (CCTA) inspection at a single center between January 2019 and March 2022.

Intervention

Machine learning models (random forest, radial basis function neural network, support vector machine, K-Nearest Neighbor, and kernel ridge regression) combining coronary artery calcification score (CACS) and 31 clinical variables.

Comparator

Comparison among the five different machine learning models.

Outcome

Prediction of coronary heart disease (CHD) probability, assessed by accuracy, sensitivity, specificity, Matthew's correlation coefficient, and area under the ROC curve.

A random forest machine learning model combining coronary artery calcification scores and clinical factors demonstrated good accuracy and high sensitivity for predicting coronary heart disease.

Abstract

OBJECTIVES: To calculate the coronary artery calcification score (CACS) obtained from coronary artery computed tomography angiography (CCTA) examination and combine it with the influencing factors of coronary artery calcification (CAC), which is then analyzed by machine learning (ML) to predict the probability of coronary heart disease(CHD). METHODS: All patients who were admitted to the Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University from January 2019 to March 2022, suspected of CHD, and underwent CCTA inspection were retrospectively selected. The degree of CAC was quantified based on the Agatston score. To compare the correlation between the CACS and clinical-related factors, we collected 31 variables, including hypertension, diabetes, smoking, hyperlipidemia, among others. ML models containing the random forest (RF), radial basis function neural network (RBFNN),support vector machine (SVM),K-Nearest Neighbor algorithm (KNN) and kernel ridge regression (KRR) were used to assess the risk of CHD based on CACS and clinical-related factors. RESULTS: Among the five ML models, RF achieves the best performance about accuracy (ACC) (78.96%), sensitivity (SN) (93.86%), specificity(Spe) (51.13%), and Matthew's correlation coefficient (MCC) (0.5192).It also has the best area under the receiver operator characteristic curve (ROC) (0.8375), which is far superior to the other four ML models. CONCLUSION: Computer ML model analysis confirmed the importance of CACS in predicting the occurrence of CHD, especially the outstanding RF model, making it another advancement of the ML model in the field of medical analysis.

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