Machine learning-aided risk stratification system for the prediction of coronary artery disease.

BACKGROUND

Machine learning (ML) may be helpful to simplify the risk stratification of coronary artery disease (CAD). The current study aims to establish a ML-aided risk stratification system to simplify the procedure of the diagnosis of CAD.

METHODS AND RESULTS

5819 patients with coronary artery angiography (CAG) from July 2015 and December 2018 in our hospital, 2583 patients (aged 56 ± 11, <50% stenosis) and 3236 patients (aged 60 ± 10, ≥50% stenosis), available on age, sex, history of smoking, systolic and diastolic blood pressure, total cholesterol level, low- and high-density lipoprotein, triglyceride level, glycosylated hemoglobin A1c and uric acid were included in the ensemble model of ML. Receiver-operating characteristic curves showed that area-under-the-curve of the training data (90%) and the testing data (10%) were 0.81 and 0.75 (P = 0.006483). The validation data of 582 patients with CAG from July 2019 to September 2019 in our hospital showed the same predictive rate of the testing data. The low-risk group (risk probability<0.2) without the treatment of hypertension, diabetes and CAD could be probably excluded the diagnosis of CAD, the moderate-risk group (risk probability 0.2-0.8) would need further examination, and high-risk group (risk probability>0.8) would suggested to perform CAG directly.

CONCLUSION

Machine learning-aided detection system with the clinical data of age, sex, history of smoking, systolic and diastolic blood pressure, total cholesterol level, low- and high-density lipoprotein, triglyceride level, glycosylated hemoglobin A1c and uric acid could be helpful for the risk stratification of prediction for the coronary artery disease.