[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128268-en":3,"doc-seo-128268-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128268,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Model to Predict the Probability of a Criminal Event in Peru Using Machine Learning Algorithms - read online free","Insecurity in the Callao region of Peru has intensified despite geographically distributed police stations, highlighting the limits of existing security strategies when advanced technologies are not integrated into prevention efforts. This study proposes a machine learning–based predictive model for Callao crime risk using a three-stage workflow: feature analysis, model design, and validation. Eight variables (F1–F8) are evaluated, and algorithms are compared using ACC, precision, F1-score, recall, and AUC. Results indicate the Decision Tree achieves the highest accuracy (ACC 0.97), followed by Gradient Boosting (0.93) and Random Forest (0.92), while K-Nearest Neighbors performs lowest (0.72), supporting Decision Tree as the most effective tool for identifying high-risk areas.","Document downloaded from the institutional repository of the University of Alcala: [http://ebuah.uah.es/dspace/](http://ebuah.uah.es/dspace/)  \nThis is a posprint version of the following published document:  \nChaucas, M., Espinoza, C., Castillo Sequera, J. L. & Wong, L. 2025,“Model to predict the probability of a criminal event in Peru using machine learning algorithms”, in 2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 259-264.  \nAvailable at [https://dx.doi.org/10.1109/ICAIBD64986.2025.11082025](https://dx.doi.org/10.1109/ICAIBD64986.2025.11082025)  \n© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.  \n(Article begins on next page)  \nModel to Predict the Probability of a Criminal Event in Peru Using Machine Learning Algorithms  \nManuel Chaucas Information Systems Engineering Program Universidad Peruana de CienciasAplicadas Lima, Perú  \n[u20201b104@upc.edu.pe](u20201b104@upc.edu.pe)  \nCarlos Espinoza Information Systems Engineering Program Universidad Peruana de CienciasAplicadas Lima, Perú  \n[u20201b085@upc.edu.pe](u20201b085@upc.edu.pe)  \nJoséLuis Castillo-Sequera Department of Computer Science Universidad deAlcalá Alcaláde Henares, Spain [jluis.castillo@uah.es](jluis.castillo@uah.es)  \nLenis Wong Information Systems Engineering Program Universidad Peruana de CienciasAplicadas Lima, Perú [pcsilewo@upc.edu.pe](pcsilewo@upc.edu.pe)  \nAbstract—Insecurity in the Callao (province of Peru) region has intensified in recent years, despite the presence of strategically distributed police stations. This issue is influenced by a lack of integration of advanced technologies in crime prevention, limiting the effectiveness of security strategies. To address this situation, the present study proposes a predictive model based on Machine Learning algorithms applied to the crime context of Callao-Peru. The methodology includes three main stages: Analysis of relevant features, model design, and results validation. In the analysis, eight variables (F1 to F8) associated with criminal patterns were evaluated, while the validation phase compared the performance of the algorithms in terms of accuracy (ACC), precision, F1-score, recall, and AUC. The results show that the Decision Tree algorithm had the highest overall accuracy, achieving an ACC of 0.97, followed by Gradient Boosting (ACC = 0.93) and Random Forest (ACC = 0.92), while K-Nearest Neighbors had the lowest performance (ACC = 0.72). These findings position the Decision Tree as the most effective tool for predicting high-risk crime areas in Callao-Peru.  \nKeywords—machine learning, criminality, crime prediction, decision tree  \nI. INTRODUCTION  \nThe police stations in the Callao region, despite being geographically well-distributed, have not managed to reduce insecurity among residents due to the lack of a comprehensive approach to crime prevention [1] . On the other hand, in Rio de Janeiro, urban violence has had a considerable adverse effect on the local economy, particularly in tourism, where a significant decrease in tourists has been observed due to the high rate of violent crimes [2] . Similarly, there is a lack of analysis to develop better technological solutions that could leverage human mobility patterns from GPS trajectory data, providing greater security along routes [3] .  \nIn Callao, insecurity remains one of the main concerns of residents, with 7,516 complaints filed in the second quarter of 2023, heightening the sense of vulnerability [4] . However, over the past year, INEI [5] recorded 4,345 complaints in Callao between July and August 2023. These crimes are primarily related to offenses against property (62.6%), public safety (13.1%),","cbCaiagngiHR69Xi","https://ap.wps.com/l/cbCaiagngiHR69Xi","pdf",823143,1,7,"English","en",105,"# Introduction\n## Insecurity and the need for integrated technologies\n## Related technological approaches\n## Research gap and study contribution\n# Related Works\n## Types of Criminal Events","[{\"question\":\"What problem does the study address in Peru?\",\"answer\":\"It targets rising insecurity in the Callao region despite the presence of police stations, focusing on how limited use of advanced technologies reduces the effectiveness of prevention strategies.\"},{\"question\":\"How is the predictive model built and validated?\",\"answer\":\"The methodology follows three stages: analyzing relevant features, designing machine learning models, and validating results by comparing algorithm performance metrics.\"},{\"question\":\"Which algorithm performs best for predicting high-risk crime areas?\",\"answer\":\"The Decision Tree algorithm achieves the highest overall accuracy (ACC = 0.97), outperforming Gradient Boosting (0.93) and Random Forest (0.92), while K-Nearest Neighbors has the lowest accuracy (0.72).\"}]","Model to Predict the Probability of a Criminal Event in Peru Using Machine Learning Algorithms - 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