Research of Medical High-Dimensional Imbalanced Data Classification Ensemble Feature Selection Algorithm with Random Forest

Abstract

The purpose of this research is to effectively classify medical data, to provide accurate data foundation for clinical diagnosis and pathology, thereby improving the prediction and discrimination of clinical diagnosis. Aiming at the problem of poor classification accuracy, caused by high-dimensional characteristics and interclass imbalance factors in clinical medical data, this paper explore a medical high-dimensional imbalanced data classification method based on random forest ensemble feature selection algorithm. A high-dimensional feature space distribution model of medical high-dimensional imbalanced data is constructed by using phase space reconstruction method. The dimension reduction is carried by K-L feature compression method, to achieve the ensemble feature optimization of medical high-dimensional imbalanced data. The selected characteristic quantity is feature-classified by random forest classification method, and the medical data with different attribute characteristics are output. The simulation results show that the proposed method can be applied to medical high-dimensional imbalanced data feature selection and classification, which has good classification accuracy, low error rate, strong anti-interclass interference capacity and good clinical application value.