Classification of physical examination indicators using multi-label classification model for mass spectrometry data

The abnormality of physical examination indexes is an essential basis for measuring the health of the human body and can help doctors make the medical diagnosis quickly. The purpose of this paper is to classify whether the physical examination indexes are abnormal or not using mass spectrometry data to assist in improving the diagnosis and identification of human system diseases. The MLSMOTE algorithm is used to solve the problem of imbalance of medical data sample categories in the experiment, and then a multi-label classification model is used to classify the data.