[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126527-en":3,"doc-seo-126527-105":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},126527,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Binary Models for Arboviruses Classification Using Machine Learning - A Benchmarking Evaluation","Arboviral diseases are common worldwide and can cause severe outcomes, including death, making early and accurate detection essential yet difficult due to overlapping clinical symptoms. This study benchmarks multiple machine learning models for classifying two arbovirus types. Two binary approaches are developed: distinguishing arbovirus infection from other diseases, and separating Dengue from Chikungunya. Models are tuned and evaluated using hyperparameter optimization and feature selection.","Proceedings of the 56th Hawaii International Conference on System Sciences | 2023  \nBinary Models for Arboviruses Classification Using Machine Learning: A  \nBenchmarking Evaluation  \nSebastio Rogerio da Silva Neto 1 , Thoms Tabosa de Oliveira 1 , Leonides Medeiros Neto 1 , Igor Vitor Teixeira 1 ,  \nSara Sadok2 , Vanderson Souza Sampaio3 , Patricia Takako Endo 1  \n1 Universidade de Pernambuco (UPE), {srsn, tto, lmn, [ivt](ivt}@ecomp.poli.br)[}](ivt}@ecomp.poli.br)[@ecomp.poli.br](ivt}@ecomp.poli.br), [patricia.endo@upe.br](patricia.endo@upe.br)  \n2 Universidad Autnoma de Barcelona, [sarasadokh@gmail.com](sarasadokh@gmail.com)  \n3 Insituto Todos pela Sa´ude (ITpS), [vandersons@gmail.com](vandersons@gmail.com)  \nAbstract  \nArboviral diseases are common worldwide. Infection with arboviruses can lead to serious health problems, even death in severe cases. Such health problems can be prevented by the early and correct detection of these arboviruses, but this is challenging due to the overlap of their symptoms. In this work, we benchmark different Machine Learning (ML) models to classify two types of arboviruses. We propose two distinct binary models: (i) a model to classify if the patient has arbovirus or another disease; and (ii) a model to classify if the patient has Dengue or Chikungunya. We configure and evaluate several ML models using hyperparameter optimization and feature selection techniques. The Random Forest and XGboost tree-based models present the best results with over 80% recall in the Chikungunya and Inconclusive classes.  \nKeywords: Arbovirus, Dengue, Chikungunya, binary model, machine learning, classification.  \n1. Introduction  \nArboviral (or arthropod-born viral) diseases are a group of diseases caused by arboviruses. These diseases are replicated in both arthropods and vertebrates, and transmitted mostly by arthropods through the bite of mosquitoes, ticks, sandflies, and midges (Shope and Meegan, 1997), as well as contaminated blood transfusion in some cases. Dengue, Chikungunya and Zika are among the diseases caused by arboviruses. According to the World Health Organization (WHO), arboviral diseases are part of a wider category, known as Neglected Tropical Diseases (NTD), which are typically prevalent in tropical locations and thrive in the poorest,  \nhardest-to-reach communities (Organization, 2022) .  \nTwo of the most common mosquitoes that transmit Dengue, Chikungunya and Zika are the Aedes Aegyptiand Aedes Albopictus (Delatte et al., 2010; Morinet al., 2013; Musso and Gubler, 2016) . These mosquitoes lay eggs in water and are adapted to human living environments. For instance, it isnot unusual the inappropriate management of water containers, trash bins, garden pots, drainage ditches, pools ditches, among others in endemic areas. As a consequence, mosquito population increases since these factors contribute to their reproduction. (LaDeau et al., 2015) .  \nSocial economic factors can be a key contributor to arbovirus diseases spread (Whiteman et al., 2020) as some habitats are ideal for mosquito growth, such as standing water containers are more likely to be found in lower income neighbourhoods (LaDeau et al., 2015; LaDeau et al., 2013) . Urban slums, marked by poor sanitation and unplanned occupation, with houses built without respecting a minimum distance, also help to increase mosquito reproduction (Liu et al., 2017) . Thus, the population of these communities are more prone to arbovirus infection.  \nArboviral diseases, specially Dengue and Chikungunya, are a global sanitary concern present in every continent (Vairo et al., 2019; Wahid et al., 2017) . One of the most affected countries by arboviral diseases is Brazil, having had many outbreaks in recent years (Barroso et al., 2020; Musso et al., 2018) .  \nThe early detection of arboviral diseases can mitigate the health damage and, in some cases, even prevent death of the infected individual (Liu et al., 2017) . However, there are some challenges to be fa","cbCaigI94RPqdqgG","https://ap.wps.com/l/cbCaigI94RPqdqgG","pdf",471191,1,10,"English","en",105,"# 1. Introduction\n## 2. Related Works\n## 3. Relevant Concepts\n## 4. Dataset and Model Design\n## 5. Results and Discussion\n## 6. Conclusion and Next Steps","[{\"question\":\"Why is arbovirus detection challenging in clinical practice?\",\"answer\":\"Arboviral diseases often present overlapping symptoms, which makes establishing a prompt and reliable diagnosis difficult.\"},{\"question\":\"What two binary models are proposed in the study?\",\"answer\":\"One model classifies whether the patient has an arbovirus or another disease. The second model distinguishes Dengue from Chikungunya.\"},{\"question\":\"Which machine learning methods show the best performance?\",\"answer\":\"The Random Forest and XGboost tree-based models achieve the best results, reporting over 80% recall in the Chikungunya and Inconclusive classes.\"}]","Binary Models for Arboviruses Classification Using Machine Learning - A Benchmarking Evaluation | PDF",1785933160,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"binary-models-for-arboviruses-classification-using-machine-learning-a-benchmarking-evaluation","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/binary-models-for-arboviruses-classification-using-machine-learning-a-benchmarking-evaluation/126527/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is arbovirus detection challenging in clinical practice?","Question",{"text":76,"@type":77},"Arboviral diseases often present overlapping symptoms, which makes establishing a prompt and reliable diagnosis difficult.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What two binary models are proposed in the study?",{"text":81,"@type":77},"One model classifies whether the patient has an arbovirus or another disease. The second model distinguishes Dengue from Chikungunya.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning methods show the best performance?",{"text":85,"@type":77},"The Random Forest and XGboost tree-based models achieve the best results, reporting over 80% recall in the Chikungunya and Inconclusive classes.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]