Data processing method for machine learning and electronic device using the same

An electronic device, comprising:

• a source quantity balancing unit, used to, for a plurality of sources, perform a source balancing procedure on an original measuring data to obtain a balanced distribution map, wherein in the balanced distribution map, a quantity of data items obtained from each of the sources is identical, the original measuring data comprises a plurality of subjects corresponding to a plurality of detection values of a plurality of detection items, the sources include different institutes or different equipment, and the source balancing procedure is used to balance the quantities of the data items obtained from different institutes or different equipment are identical;
• a personalization scaling unit used to, for each of the subjects, perform a personalization scaling procedure on the detection values to obtain a personalized scaled measuring data, wherein in the personalized scaled measuring data, the detection values of each of the subjects are scaled to identical numeric interval;
• a source scaling unit used to, for each of the sources, perform a source scaling procedure on the detection values to obtain a by-source scaled measuring data, wherein in the by-source scaled measuring data, the detection values of each of the sources are scaled to identical numeric interval, and the detection values of each of the institutes or equipment are scaled to identical numeric interval;
• a combination unit used to combine the balanced distribution map, the personalized scaled measuring data and the by-source scaled measuring data to obtain a balanced personalized scaled data and a balanced by-source scaled data;
• and
• an extraction unit, comprising:
• a splitter used to split the balanced personalized scaled data and the balanced by-source scaled data into a plurality of splits, each corresponding to all of the sources;
• a calculator used to sample each of the splits perform and obtain a predictive ability table through analysis, wherein the predictive ability table comprises a predictive ability of each of the detection items;
• and
• a selector used to, based on the predictive ability table, output some of the detection items, wherein the outputted detection items are used for a machine learning model to perform model building, training or prediction inference.