BS-SC Model:A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

作     者：Saravanan Parthasarathy Arun Raj Lakshminarayanan 

作者机构：s.abdur rahman crescent institute of science and technologygst roadvandalurchennai600048tamil naduindia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第8期

页      面：1311-1336页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

主　　题：Child abuse sexual offending decision-making machine learning stacking classifier 

摘      要：For a long time,legal entities have developed and used crime prediction *** techniques are frequently updated based on crime evaluations and responses from scientific *** is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup *** maltreatment is not adequately addressed because children are *** a result,the possibility of developing a model for predicting child abuse was investigated in this *** exploratory analysis methods were used to examine the city of Chicago’s child abuse *** data set was balanced using the Borderline-SMOTE technique,and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child *** proposed approach successfully predicted crime types with 93%of accuracy,precision,recall,and *** AUC value of the same was ***,when compared to the Extra Trees model(17.55),which is the second best,the proposed model’s execution time was significantly longer(476.63).We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child *** results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model(BS-SC Model)would be effective in the real-time child abuse prediction and prevention process.