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

作　　者：丁海媛 杜宇[1] 李昊洋 郑丽娜 张金建 DING Hai-yuan;DU Yu;LI Hao-yang;ZHENG Li-na;ZHANG Jin-jian(Department of Forensic Science of Criminal Investigation Police University of China,Shenyang 110854,China)

机构地区：[1]中国刑事警察学院刑事科学技术学院,辽宁沈阳110854

出　　处：《分析测试学报》2025年第3期420-428,共9页Journal of Instrumental Analysis

基　　金：公安部技术研究计划项目(2022JSYJC25);辽宁省自然科学基金项目(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)。

摘　　要：为提高使用血液生化指标推断溺液温度的准确性,该研究使用溺死SD大鼠新鲜心血进行生化检验,利用统计检验和机器学习构建了溺液温度与血液生化指标的回归模型。通过测定血液的14种生化指标,对数据进行正态性检验、方差齐性检验、方差检验、事后检验和相关性分析,并使用K-最近邻回归(KNN)模型筛选重要特征指标,进行基准测试和建立回归模型,最后对模型进行调参、评估和解释。结果显示,14种生化指标在不同温度下均符合正态分布、其组内均值存在显著差异(p<0.05),有18.6%的组两两比较差异不显著;各个指标之间的共线性程度较低(<70%);回归模型筛选出碱性磷酸酶(ALP)、谷氨酰转肽酶(GGT)、肌酐(Cr)、尿酸(UA)、尿素(UREA)、高密度脂蛋白胆固醇(HDL_C)、胆固醇(CHO)、甘油三酯(TG)、Ca^(2+)和Mg^(2+)10种生化指标,以其为特征变量建立的KNN回归模型经过超参数调优后测试集的均方根误差(RMSE)为1.872,决定系数(R2)为0.9792,Mg^(2+)、TG、ALP三个指标特征对模型的影响最大,且均为负作用。与传统的回归模型相比,该研究建立的回归模型能够充分利用样本数据,数据前处理简单、准确性高、可解释性好,适合溺液温度的推断。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 subjected 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 randomly divided into four experimental groups(based on water temperature during drowning):cold water 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 drowning 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(2500 r/min for 10 minutes at 4℃)and transferred to labeled EP tubes.From each tube,200μL of serum was extracted into a dedicated serum cup for biochemical analysis.Using the Chinese Inova DS-401 automated biochemical 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(Ca^(2+)),and magnesium ion(Mg^(2+)).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.Results showed that all 14 biochemical indicators conformed to normal distr

关 键 词：溺液温度 血液生化 溺死 机器学习 

分 类 号：O615.4[理学—无机化学] TB9[理学—化学]