In recent years, the application of information technology and electronic medical records and medical records in medical institutions has become more and more widespread, which has resulted in a large amount of medical data in hospital databases. Decision tree is widely used in medical data analysis because of its high classification precision, fast calculation speed, and simple and easily understood classification rules. However, due to the inherent high dimensional feature space and high feature redundancy of medical data, the classification precision of traditional decision trees is low. Based on this, this paper proposes a hybrid feature selection algorithm (GRRGA) that combines information gain ratio ranking grouping and group evolution genetic algorithm. Firstly, the information gain ratio based filtering algorithm is used to sort the original feature set; then, the ranked features are grouped according to the density principle of equal division; finally, a group evolution genetic algorithm is used to perform a search on the ranked feature groups. There are two kinds of evolution methods: in-population and out-population, which use two different fitness functions to control the evolution process in group evolution genetic algorithm. The experimental results show that the average precision index of the GRRGA algorithm on the six UCI datasets is 87.13%, which is significantly better than the traditional feature selection algorithm. In addition, compared with the other two classification algorithms, the feature selection performance of the GRRGA algorithm proposed in this study is optimal. More importantly, the precision index of the bagging method on the arrhythmia and cancer medical datasets is 84.7% and 78.7% respectively, which fully proves the practical application significance of the proposed algorithm.