Training data processing method and electronic device

An electronic device, comprising:

• an input circuit;
• and
• a processor, coupled to the input circuit, wherein
• the input circuit obtains medical history data comprising at least one first disease suffered by a user,
• the processor sets a plurality of disease types according to a target disease,
• the processor sets a time interval, wherein the user does not suffer from the target disease, wherein the time interval is between a third time point and a fourth time point, the third time point is Z years ago before a time point, wherein the time point is when the medical history data is obtained, the fourth time point is X years ago before the third time point, and Z and X are positive numbers,
• the processor obtains at least one second disease in the time interval from the medical history data,
• the processor performs a pre-processing operation on the second disease according to the disease types to obtain processed data,
• the processor inputs the processed data to a neural network to train the neural network,
• the trained neural network receives the medical history data of a subject, and
• the trained neural network determines whether the subject will be diagnosed with the target disease, wherein a prediction effect of the trained neural network is enhanced by training with the processed data,
• wherein in the operation of performing the pre-processing operation on the at least one second disease according to the disease types to obtain the processed data,
• the processor weights each of the at least one second disease, and
• the processor respectively converts the weighted at least one second disease into at least one piece of word frequency information and treats the word frequency information as the processed data,
• wherein the processor weights the at least one second disease based on one of below:
• (a) whether the at least one second disease has been diagnosed;
• (b) a number of visits for the at least one second disease;
• (c) other medical history information;
• (d) disease dosages;
• (e) a disease importance sorted through a machine learning method.