[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119412-en":3,"doc-seo-119412-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119412,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Classifying Divorce Cases in Iranian Judiciary Courts Using Machine Learning - A Predictive Perspective","This study develops a machine learning model to predict the classification of divorce cases in Iranian Judiciary Courts based on socioeconomic factors. Data collected between 2011 and 2018 are used to train and evaluate several algorithms under a rigorous 10-fold cross-validation framework. Findings show Random Forest and Neural Network classifiers achieve the highest accuracy. Key socioeconomic drivers such as unemployment rate and urbanization rate are identified, supporting data-driven guidance for social policy design and resource allocation.","Journal of Sciences, Islamic Republic of Iran 35(2): 147-157 (2024) [http://jsciences.ut.ac.ir](http://jsciences.ut.ac.ir)  \n[University of Tehran](University of Tehran), [ISSN 1016-1104](ISSN 1016-1104)  \nClassifying Divorce Cases in Iranian Judiciary Courts Using Machine Learning: A Predictive Perspective  \nE. Tabrizi 1* and M. A. Farzammehr2  \n1 Department of Mathematics, Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Islamic Republic of Iran.  \n2 Judiciary Research Institute, Tehran, Islamic Republic of Iran  \nReceived: 3 October 2024 / Revised: 1 January 2025 / Accepted: 22 January 2025  \nAbstract  \nThis study develops a machine learning model to predict the classification of divorce cases in Iranian Judiciary Courts based on socioeconomic factors. Using data collected between 2011 and 2018 and various machine learning algorithms, the study evaluates the performance of predictive models through a rigorous 10-fold cross-validation process. Results highlight the Random Forest and Neural Network classifiers as the most accurate. Key socioeconomic factors influencing divorce cases, such as unemployment rate and urbanization rate, are identified. The findings provide actionable insights for policymakers to develop data-driven strategies for social policy and resource allocation.  \nKeywords: Divorce Cases; Data Mining; Machine Learning Techniques; Iran; Judiciary.  \nIntroduction  \nIn recent years, Iran has witnessed a significant increase in divorce rates, which has become a major concern for policymakers and society as a whole. According to the latest available data from the Statistical Center of Iran, the divorce rate in the country has risen from 8.7 per 1,000 marriages in 2006 to 20.8 per 1,000 marriages in 2020 (Statistical Center of Iran, 2020) .  \nGiven the social and economic impacts of divorce on families and society, it is crucial to predict divorce trends in civil courts in order to anticipate the demand for legal services and allocate resources accordingly. Accurately predicting divorce cases in civil courts can assist policymakers and court officials in planning for future caseloads, allocating resources, and developing  \neffective policies and programs to support families undergoing divorce.  \nPredictive modeling using machine learning techniques offers a promising approach to forecasting divorce trends in civil courts, as it can consider a wide range of socioeconomic factors and identify the most important predictors of divorce rates. This information can then be used to inform policy decisions and develop targeted interventions to support families at risk of divorce (1) .  \nMachine learning algorithms use statistical and computational techniques to identify patterns and relationships in large datasets and use these patterns to make predictions on new data. In this case, the algorithm would utilize historical data on divorce ratesand socioeconomic factors to build a predictive model capable of forecasting future trends in divorce rates (2,  \n*  \nCorresponding Author: Tel: +98 21-88329220; Email: [elham.tabrizi@khu.ac.ir](elham.tabrizi@khu.ac.ir)  \nVol. 35 No. 2 Spring 2024 E. Tabrizi and M. A. Farzammehr. J. Sci. I. R. Iran  \n3) .  \nMachine learning offers several advantages over traditional methods, such as improved accuracy and the ability to handle large and complex datasets. The use of machine learning in predictive modeling can also identify important predictors that may not be obvious using conventional methods and can provide insights into the underlying factors driving divorce trends in Iran.  \nOverall, the use of machine learning in predictive modeling can help policymakers and practitioners in Iranian civil courts better understand and prepare for future changes in the volume of divorce cases, as well as develop more effective policies and interventions to address the social and economic issues associated with divorce.  \nThe main innovation of this study lies in the use of a u","cbCaiaEffHcewroM","https://ap.wps.com/l/cbCaiaEffHcewroM","pdf",1247906,1,11,"English","en",105,"# Abstract\n# Introduction\n## Motivation and need for prediction\n## Machine learning for forecasting\n# Literature Review\n## Socioeconomic factors\n## Demographic factors\n# Methods and Evaluation (implied)\n## Dataset and classification algorithms\n## 10-fold cross-validation","[{\"question\":\"What is the main goal of the study on Iranian divorce cases?\",\"answer\":\"To build a machine learning model that predicts the classification of divorce cases using socioeconomic factors in Iranian Judiciary Courts.\"},{\"question\":\"Which algorithms performed best in the reported results?\",\"answer\":\"The Random Forest and Neural Network classifiers showed the most accurate performance in the evaluation.\"},{\"question\":\"How were predictive models evaluated in the study?\",\"answer\":\"Model performance was assessed using a rigorous 10-fold cross-validation process on data collected from 2011 to 2018.\"}]","Classifying Divorce Cases in Iranian Judiciary Courts Using Machine Learning - 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