Abstract: An electronic device and a method for selecting a feature of an electrocardiogram (ECG) are provided. The method includes: obtaining the ECG; performing a first pre-processing on the ECG to generate a first ECG; marking multiple extreme points corresponding to at least one type of wave on the first ECG; calculating a first feature value corresponding to a first feature according to the multiple extreme points of the at least one type of wave, generating a first performance index corresponding to a machine learning model according to the first feature value, and determining whether to select the first feature according to the first performance index; and outputting the first feature in response to selecting the first feature.

Abstract: An electronic device and a method for screening features for predicting a physiological state are provided. The method includes: obtaining multiple physiological data corresponding to multiple features; generating multiple first subsets of the multiple features according to the multiple physiological data based on a first model, wherein the multiple first subsets respectively correspond to the multiple physiological data; selecting a first feature from the multiple features according to the multiple first subsets, calculating a first relation index of the first feature and a second feature corresponding to the multiple features, and selecting the second feature as an accompanied feature of the first feature according to the first relation index; and outputting the first feature and the accompanied feature.

Abstract: An electronic device and a method for predicting a blockage of a coronary artery are provided. The method includes: obtaining multiple pieces of electrocardiogram (ECG) data respectively corresponding to a coronary artery set; generating multiple first probabilities corresponding to the multiple pieces of electrocardiogram data respectively according to the multiple pieces of electrocardiogram data and a first phase model, generating a first determined result according to the multiple first probabilities, and selecting a first data subset corresponding to a first probability subset from the multiple pieces of electrocardiogram data in response to each one in the first data subset of the multiple first probabilities being greater than a first threshold; generating multiple second probabilities corresponding to the first data subset according to the first data subset and a second phase model, and generating a second determined result according to the multiple second probabilities.

Abstract: A physiological status evaluation method and a physiological status evaluation apparatus are provided. The method includes the following: obtaining original electrocardiogram data of a user by an electrocardiogram detection apparatus; converting the original electrocardiogram data into digital integration data; obtaining a plurality of physiological characteristic parameters according to the digital integration data; filtering the physiological characteristic parameters for at least one notable characteristic parameter through at least one filter model, where decision importance of the at least one notable characteristic parameter in a decision process of the at least one filter model is greater than a threshold; building a prediction model according to the at least one notable characteristic parameter; and evaluating a physiological status of the user through the prediction model.

Abstract: The disclosure provides a disease classification method and a disease classification device. The disease classification method includes: inputting samples into a first stage model and obtaining a first stage determination result; inputting first samples determined positive by the first stage model into a second stage high specificity model to obtain second samples determined to be positive and third samples determined to be negative and rule in the second samples; inputting fourth samples determined negative by the first stage model into a second stage high sensitivity model to obtain fifth samples determined to be positive and sixth samples determined to be negative and rule out the sixth samples; obtaining a second stage determination result of the second and sixth samples; and inputting the third and fifth samples not ruled in or ruled out into a third stage model and obtaining a third stage determination result of the third and fifth samples.