Abstract: Human body surface area (BSA) plays a crucial role in clinical medicine, but most of the existing BSA formulas only use two parameters: height and weight, and adopt the method of matching simple function to estimate the body surface area. Doctors also show that the existing clinical BSA formulas have a large calculation error. To solve these problems, a new BSA regression prediction model is proposed. The regression model consists of two parts: firstly, the factors of body surface area with high correlation are selected by correlation and significance analysis; secondly, a regression model is constructed by training the deep feed-forward neural network with 104 sets of human body data. 5-fold cross validation and independent test set and two verification methods are adopted in the experiments. Firstly, the accuracy of the deep feedforward neural network model and the traditional human surface area calculation formula are evaluated, and the results are compared and analyzed. Secondly, the accuracy of the deep feedforward neural network model and the three algorithm models are evaluated, and the results are compared and analyzed. Compared with the traditional methods, the determination coefficient of the deep feedforward neural network model is higher than that of the two traditional methods, and is six percentage points higher than the traditional method with better results, and the error of deep feedforward neural network model is nearly twice as low as that of the traditional method. Compared with the three algorithm models, the deep feedforward neural network improves the determination coefficient by two percentage points and reduce the error. The experimental results of consistency analysis also show that the 95% consistency limit of the deep feedforward neural network is the smallest and the consistency is the best. Through above experiments, it is proved that the proposed regression framework can better calculate body surface area and obtain more accurate prediction value. 
Key words:body surface area;deep feedforward neural network;regression;prediction;cross- validation