GARD: Gender difference analysis and recognition based on machine learning

Abstract

In recent years, intelligent diagnosis and intelligent medical treatment based on big data of medical examinations have become the main trend of medical development in the future. In this paper, we propose a method for analyzing the difference between males and females in medical examination items (medical attributes) and find that males and females of different ages have differences in medical attributes. Then, the cluster analysis method is used to further analyze the differences between male and female in medical examination items, such that some common important attributes (CIAs) that can be used for gender recognition are found within a specific age range. Following, we propose two gender recognition models (GRMs) by using the found CIAs to identify the gender. A large number of experimental results are provided to validate the effectiveness of the proposed GRMs. Experimental results show that the medical attributes with a large value of difference really contribute to gender recognition. Within a certain age range, such as 17 to 51 years old, the proposed GRM can reach 92.8% accuracy using only six medical attributes.