Objective Male pattern baldness (MPB), or androgenetic alopecia (AGA), is a common type of hair loss in men, with an estimation that approximately 80% of the phenotypic variance can be explained by genetic factors. Most prediction models were developed in European and few MPB associated (single nucleotide polymorphisms,SNPs) have been validated in East Asian population. In this study, MPB associated SNPs in European were verified in Chinese population, and MPB risk prediction models were built based on those SNP data.Methods We examined 486 genetic variants previously reported associated with MPB, and assessed their impacts on hair loss in 312 Chinese individuals. Different sets of SNPs were selected by stepwise regression and Lasso regression. Logistic regression algorithm was used to construct the prediction models and the evaluations were conducted by the method of 10-fold cross validation. We further compared the prediction accuracy among logistic regression, k-nearest neighbor classifier, random forest and support vector machine.Results 174 SNPs demonstrated significant associations with MPB (P<0.05). Among those SNP markers, 22 SNPs and 25 SNPs were selected by different screening methods. Two logistic regression model considering the genotypes of 22 and 25 SNPs demonstrate that the risk of MPB were predictable at AUC (area under curve) level of 0.85 and 0.84. Prediction accuracy was slightly reduced after performing 10-fold cross validation, 0.81 and 0.77 respectively. Moreover, the AUC of both models reaches maximum (0.89) when age was added as a predictive factor. From the running results, the logistic regression prediction model had obvious advantages.Conclusion Overall, although the accuracy obtained here has not reached a clinically desired level, our model still has great potential for genetic prediction of MPB, which may assist decision making on early MPB intervention actions and in forensic investigations.