Lung adenocarcinoma machine learning model based on microRNA sequencing abundance

The invention discloses a lung adenocarcinoma machine learning classification model based on microRNA (miRNA) sequencing abundance, and the method comprises the steps: 1, analyzing miRNA high-throughput sequencing data of a lung adenocarcinoma tissue sample and a paracancerous normal tissue sample, and obtaining the abundance value of mature miRNA in a sequencing file; step 2, carrying out batch effect correction and standardization processing on the abundance value matrix; 3, three miRNAs are selected through a DFL algorithm, the three miRNAs are hsa-miR-200b-5p, hsa-miR-126-5p and hsa-miR-30c-2-3p respectively, a machine learning classification model used for predicting lung adenocarcinoma is constructed for abundance value data corresponding to the three miRNAs through five classification algorithms including K-proximity, a decision tree, a random forest, a support vector machine and DFL, and the accuracy rates are 100%, 98.6%, 100%, 98.6% and 100% respectively. 本发明公开了一种基于microRNA（miRNA）测序丰度的肺腺癌机器学习分类模型，其方