目的基于机器学习随机森林算法建立青少年心理危机预测模型,分析青少年心理危机的影响因素。方法分别在2020年11月与2021年6月,采用整群抽样追踪调查1417名中学生,第一次测量收集人口学资料、症状因素、保护因素等问卷数据,第二次测量...目的基于机器学习随机森林算法建立青少年心理危机预测模型,分析青少年心理危机的影响因素。方法分别在2020年11月与2021年6月,采用整群抽样追踪调查1417名中学生,第一次测量收集人口学资料、症状因素、保护因素等问卷数据,第二次测量抑郁、自杀风险,以是否在第二次测量中呈现中度以上抑郁(抑郁得分≥15分)与高自杀风险(自杀风险得分≥7分)为心理危机判定标准。运用SPSS 24.0进行统计学分析,采用R version 4.1.1软件构建青少年心理危机随机森林机器学习预测模型,并分析青少年出现心理危机的高预估因素。结果(1)中度以上抑郁检出率为10.02%(142/1417),高自杀风险检出率为30.77%(436/1417),心理危机检出率为8.19%(116/1417)。(2)心理危机预测模型敏感度为0.79,特异度为0.82,阳性预测值为0.82,阴性预测值为0.79,准确率为0.80,曲线下面积为0.88。(3)青少年心理危机影响因素排名前十的特征变量依次为抑郁情绪、焦虑情绪、自杀意念、自我伤害行为、认知灵活性-可控性、认知灵活性-可选择性、坚毅-坚持努力、坚毅-兴趣一致性、母亲情绪和父亲情绪(模型预测精准度=0.023~0.163)。结论青少年心理危机的发生与症状因素、保护因素、父母情绪关系密切,且有跨时间预估的意义。机器学习随机森林算法能有效识别心理危机个体,识别敏感的危机个体特征。展开更多
Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm.Financial crisis prediction(FCP)is an essen...Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm.Financial crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution.At the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical world.The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process.This paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT environment.The MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and classification.Primarily,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further analysis.In addition,SMOTE technique is employed to handle class imbalance problems.The goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of SMOTE.Besides,SAE model is utilized as a classification technique to determine the class label of the financial data.At the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the SAE.An extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct aspects.The experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.展开更多
文摘目的基于机器学习随机森林算法建立青少年心理危机预测模型,分析青少年心理危机的影响因素。方法分别在2020年11月与2021年6月,采用整群抽样追踪调查1417名中学生,第一次测量收集人口学资料、症状因素、保护因素等问卷数据,第二次测量抑郁、自杀风险,以是否在第二次测量中呈现中度以上抑郁(抑郁得分≥15分)与高自杀风险(自杀风险得分≥7分)为心理危机判定标准。运用SPSS 24.0进行统计学分析,采用R version 4.1.1软件构建青少年心理危机随机森林机器学习预测模型,并分析青少年出现心理危机的高预估因素。结果(1)中度以上抑郁检出率为10.02%(142/1417),高自杀风险检出率为30.77%(436/1417),心理危机检出率为8.19%(116/1417)。(2)心理危机预测模型敏感度为0.79,特异度为0.82,阳性预测值为0.82,阴性预测值为0.79,准确率为0.80,曲线下面积为0.88。(3)青少年心理危机影响因素排名前十的特征变量依次为抑郁情绪、焦虑情绪、自杀意念、自我伤害行为、认知灵活性-可控性、认知灵活性-可选择性、坚毅-坚持努力、坚毅-兴趣一致性、母亲情绪和父亲情绪(模型预测精准度=0.023~0.163)。结论青少年心理危机的发生与症状因素、保护因素、父母情绪关系密切,且有跨时间预估的意义。机器学习随机森林算法能有效识别心理危机个体,识别敏感的危机个体特征。
文摘Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm.Financial crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution.At the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical world.The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process.This paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT environment.The MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and classification.Primarily,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further analysis.In addition,SMOTE technique is employed to handle class imbalance problems.The goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of SMOTE.Besides,SAE model is utilized as a classification technique to determine the class label of the financial data.At the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the SAE.An extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct aspects.The experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.
