Abstract: A leukemia molecular prediction model is constructed by using bioinformatics and machine learning methods with gene expression profile.Firstly,three methods including relief,classification information index and information gain index are used to select candidate feature gene set from the leukemia gene expression profile.Secondly,intersection of three candidate feature gene sets is generated,and then the best classification performance of intersection genes which is tested by SVM is selected as feature genes.Thirdly, the classification rule sets are extracted from these feature genes by using decision tree method.Finally,the leukemia molecular prediction model is constructed with these classification rules.The results show that the model is helpful to cancer clinical diagnosis and cancer gene biological experiments.Also,the two key genes (CD33,MPO)are biomarkers of leukemia clinically.