SHAP analysis-guided interpretable inference modeling for wound age estimation
10.13618/j.issn.1001-5728.2024.02.014
- VernacularTitle:SHAP分析指导的早期损伤时间可解释推断模型构建
- Author:
Huimin LV
1
;
Mingfeng LIU
;
Qianqian JIN
;
Yibo ZHANG
;
Guoshuai AN
;
Qiuxiang DU
;
Yingyuan WANG
;
Junhong SUN
Author Information
1. 山西医科大学法医学院,山西 晋中 030600
- Keywords:
Forensic pathology;
Wound age estimation;
Machine learning algorithms;
SHAP;
Feature explanation
- From:
Chinese Journal of Forensic Medicine
2024;39(3):320-326
- CountryChina
- Language:Chinese
-
Abstract:
Objective To address the challenges of poor performance and lack of interpretability in existing models,the SHAP algorithm is used to develop an interpretable machine learning model that offers a novel approach to wound age estimation,Methods Based on the previous discovery of the expression of 35 wound age healing-related genes in contused skeletal muscle,the woun age estimaton model was constructed using four algorithms,namly,Multilayer Perceptron(MLP),Random Forest(RF),LightGBM(LGBM),and Support Vector Machine(SVM).The SHAP(Shapley Additive Explanation)algorithm was used to rank the importance of genetic features,eliminate redundant attributes,and optimize the model for accurate wound age estimation.the genetic features of the optimal model were analyzed using SHAP's local interpretation capabilities.Results The best results were obtained using model of MLP(area under the curve(AUC)=0.99)The wound ages were classified into four categories:4～12 h,16～24 h,28～36 h,and 40～48 h,using only 15 gene features.According to SHAP analysis,Fam210a was identified as the most relevant gene.Local analysis revealed that high expression of Fam210a contributed to an increase in the predicted probability of 4 h～12 h,while high expression of Rae1 contributed to an increase in the predicted probability of 16 h～24 h.Additionally,low expression of Tbx18 contributed to an increase in the predicted probability of 28 h～36 h,whereas high expression of Tbx18 contributed to an increase in the predicted probability of 40 h～48 h.Conclusions The combined MLP and SHAP model can be used to predict wound age.Using the SHAP interpreter can better understand the degree of contribution of feature genes to the model prediction,and lay the foundation for further in-depth study of wound age estimation.
