Random forest classifier improving phenylketonuria screening performance in two Chinese populations
Yingnan Song, Zhe Yin, Chuan Zhang, Shengju Hao, Haibo Li, Shifan Wang, Xiangchun Yang, Qiong Li, Danyan Zhuang, Xinyuan Zhang, Zongfu Cao, Xu Ma
Abstract
Phenylketonuria (PKU) is a genetic disorder with amino acid metabolic defect, which does great harms to the development of newborns and children. Early diagnosis and treatment can effectively prevent the disease progression. Here we developed a PKU screening model using random forest classifier (RFC) to improve PKU screening performance with excellent sensitivity, false positive rate (FPR) and positive predictive value (PPV) in all the validation dataset and two testing Chinese populations. RFC represented outstanding advantages comparing several different classification models based on machine learning and the traditional logistic regression model. RFC is promising to be applied to neonatal PKU screening.