Classification and prediction of spinal disease based on the SMOTE-RFE-XGBoost model

ABSTRACT: Spinal diseases are killers that cause long-term disturbance to people with complex and diverse symptoms and may cause other conditions. At present, the diagnosis and treatment of the main diseases mainly depend on the professional level and clinical experience of doctors, which is a breakthrough problem in the field of medicine. This article proposes the SMOTE-RFE-XGBoost model, which takes the physical angle of human bone as the research index for feature selection and classification model construction to predict spinal diseases. The research process is as follows: two groups of people with normal and abnormal spine conditions are taken as the research objects of this article, and the synthetic minority oversampling technique (SMOTE) algorithm is used to address category imbalance. Three

SUBMITTER: Zhang B 

PROVIDER: S-EPMC10280425 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature