Research on data dimension reduction based on genetic algorithm in cancer prediction

When using high-dimensional data for data analysis and processing, problems such as complex data processing algorithms, computational time and resource consumption, and poor model interpretability are often faced. This article proposes a data dimension reduction method based on genetic algorithms for breast cancer tumor prediction problems. The genetic algorithm is used to reduce the dimension and optimize the original 30 features in the data set, and cross-validation is used to evaluate the model prediction results, Select the optimal features, then the selected features are used for prediction evaluation. The experimental results show that this method can significantly reduce the dimension of the model input parameters and improve the accuracy of the model prediction to a certain extent.