The application discloses a
coronary heart disease prediction method fusing cardiopulmonary exercise parameter
time sequence information, and comprises the following steps: collecting multidimensional
health data of a patient; preprocessing the multidimensional
health data; extracting features from the preprocessed data; performing
time sequence analysis on the extracted features by using a
deep learning model; inputting the
time sequence analysis result and static features into a classification model to perform
coronary heart disease prediction, outputting a prediction result and generating a related health report. By acquiring
demographic data, basic
medical information and cardiopulmonary exercise
test data of a target object, specifically including age, gender,
body mass index, socio-economic information,
past history, complications, clinical examination and inspection,
exercise capacity, cardiovascular function and ventilation
perfusion and the like, the
coronary heart disease is predicted by fusing static parameters and time sequence information, so that dynamic changes in the
health data of the patient can be better captured, and the prediction accuracy is improved.