分析测试学报2025，Vol.44Issue(3)：420-428,9.DOI:10.12452/j.fxcsxb.240725253
基于可解释机器学习的SD大鼠溺液温度与血液生化指标变化预测模型的研究
Research on a Prediction Model of Temperature of Drowning Fluid and Blood Biochemical Indicators in SD Rats Based on Interpreta-ble Machine Learning
摘要
Abstract
To enhance the accuracy of inferring drowning fluid temperature using blood biochemical indicators,this study conducted biochemical tests on fresh cardiac blood samples from SD rats sub-jected to drowning.Statistical tests and machine learning were employed to develop regression models linking drowning fluid temperature to blood biochemical markers.A total of 250 male rats were ran-domly divided into four experimental groups(based on water temperature during drowning):cold wa-ter drowning(8-10℃),normal temperature drowning(20-22℃),warm water drowning(30℃),and hot water drowning(45℃),along with a control group(cervical dislocation,n=50 per group).Rats in the experimental groups were individually immersed in pre-adjusted water tanks until drown-ing occurred.Immediately after death,the chest cavity of each rat was opened,and approximately 2 mL of right heart blood was rapidly collected.Blood samples were left to stand at room temperature for 1.5 hours before serum supernatant was separated by centrifugation(2 500 r/min for 10 minutes at 4℃)and transferred to labeled EP tubes.From each tube,200 µL of serum was extracted into a dedicated serum cup for biochemical analysis.Using the Chinese Inova DS-401 automated biochemi-cal analyzer,14 biochemical markers were tested in the samples:alanine aminotransferase(ALT),aspartate aminotransferase(AST),alkaline phosphatase(ALP),gamma-glutamyl transferase(GGT),creatinine(Cr),uric acid(UA),urea,glucose(GLU),high-density lipoprotein cholesterol(HDL_C),low-density lipoprotein cholesterol(LDL_C),cholesterol(CHO),triglycerides(TG),calcium ion(Ca2+),and magnesium ion(Mg2+).Normality,homogeneity of variance,ANOVA,post-hoc tests,and correlation analyses were performed on the 14 biochemical indicators.KNN regression models were then utilized to screen critical features,benchmark tests were conducted,regression models were established,and finally,model tuning,evaluation,and interpretation were performed.Re-sults showed that all 14 biochemical indicators conformed to normal distribution across different tem-perature groups,with significant differences in mean values within groups(p<0.05),albeit 18.6%of pairwise comparisons exhibited non-significant differences.Low collinearity(<70%)was observed among indicators.The regression model selected ALP,GGT,Cr,urea,UA,HDL_C,CHO,TG,Ca2+,and Mg2+as the 10 most important biochemical markers.The KNN regression model built with these variables,after hyperparameter optimization,achieved RMSE=1.872 and R2=0.979 2 on the test set,with Mg2+,TG,and ALP having the greatest negative impact on the model.Compared to traditional regression models,the model established in this study fully leverages sample data,of-fering simplicity in data preprocessing,high accuracy,and good interpretability,making it suitable for inferring drowning fluid temperature.关键词
溺液温度/血液生化/溺死/机器学习Key words
drowning fluid temperature/blood biochemistry/drowning/machine learning分类
化学引用本文复制引用
丁海媛,杜宇,李昊洋,郑丽娜,张金建..基于可解释机器学习的SD大鼠溺液温度与血液生化指标变化预测模型的研究[J].分析测试学报,2025,44(3):420-428,9.基金项目
公安部技术研究计划项目(2022JSYJC25) （2022JSYJC25）
辽宁省自然科学基金项目(2021-MS-143) （2021-MS-143）
上海市刑事科学技术研究院现场物证重点实验室开放课题资助项目(2020XCWZK10) （2020XCWZK10）
