Abstract

Foot & ankle disfigurement is a habitual complaint with high prevalence and is stylish treated in nonage. still, the current individual procedures calculate on croaker discussion and empirical judgment, and warrant objective and quantitative evaluation styles, performing in low webbing rates. To break this problem, this paper aims to construct an evaluation model for children’s bottom & ankle disfigurement through data mining and machine literacy technologies. Originally, it proposes the grading rules for children’s bottom & ankle disfigurement inflexibility grounded on assaying the being quantitative indicators and expert experience. also the 3D bottom scanner is used to collect the sample data including 30 bottom structure indicators. Eventually, an advanced meager multi-objective evolutionary algorithm (meager MO- FS) is present for point selection.