[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126303-en":3,"doc-seo-126303-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126303,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",7,"Healthcare","Comparison of Machine Learning as an Inference Engine to Improve Expert Systems in Dengue Disease","Dengue disease remains a major public health challenge in tropical and subtropical regions, with incidence and mortality increasing over recent decades. Existing expert systems for early detection often use rigid rule-based inference engines that depend on expert-defined structures and cannot adapt to evolving knowledge. This study integrates machine learning into the inference engine using medical record data as a dynamic knowledge source, improving flexibility and adaptability. Using 90 records balanced to 126 with SMOTE, Decision Trees, SVM, and ANN achieved average accuracy, precision, recall, and F1 of 97.73%, 98.33%, 97.22%, and 97.41%, respectively. The adaptive framework reduces reliance on static rule bases and supports scalable diagnostics for dengue and other infectious diseases.","INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION  \n[journal homepage :](journal homepage : www.joiv.org/index.php/joiv)[ www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv)  \nComparison of Machine Learning as an Inference Engine to Improve  \nExpert Systems in Dengue Disease  \nIstiadi a,*, Fitri Marisa a, Rudy Joegijantorob, Affi Nizar Suksmawati a, Aviv Yuniar Rahman a,c  \na Department of Informatics Engineering, Universitas Widya Gama Malang, Lowokwaru, Malang, Indonesia b Department of Environmental Health Science, Widyagama Husada College of Health, Lowokwaru, Malang, Indonesia c School of Graduate Studies, PhD (ICT), Asia e University, Subang Jaya, Selangor, Malaysia  \nCorresponding author:*[istiadi@widyagama.ac.id](istiadi@widyagama.ac.id)  \nAbstract—Dengue disease remains a significant public health challenge in tropical and subtropical regions, with rising incidence and mortality rates over the past few decades. While expert systems have been developed for early detection, traditional approaches often rely on rigid rule-based inference engines, which are limited by their dependence on expert-defined structures and lack adaptability to evolving knowledge sources. This study introduces a novel approach to enhance the flexibility and adaptability of expert systems by integrating machine learning (ML) techniques into the inference engine, leveraging the growing availability of medical record data asa dynamic knowledge source. Using a dataset of 90 medical records, balanced to 126 items via the Synthetic Minority Over-sampling Technique (SMOTE), we evaluated the performance of multiple ML algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), against traditional models like Naive Bayes (NB) and K-Nearest Neighbors (KNN). The DT, SVM, and ANN models demonstrated exceptional performance, achieving average accuracy, precision, recall, and F1 scores of 97.73%, 98.33%, 97.22%, and 97.41%, respectively. The key innovation of this research lies in developing an adaptive inference engine that can dynamically learn from medical data, reducing reliance on static rule bases and enabling the expert system to evolve with new knowledge. This approach improves diagnostic accuracy and provides a scalable and flexible framework for addressing other infectious diseases. By bridging the gap between expert systems and machine learning, this study paves the way for more intelligent, data-driven healthcare solutions with significant implications for public health and disease management.  \nKeywords—Expert system; machine learning; inference engine; dengue disease.  \nManuscript received 30 Sep. 2024; revised 17 Dec. 2024; accepted 15 Feb. 2025. Date of publication 31 May 2025.  \nInternational Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nI. INTRODUCTION  \nDengue fever remains a significant public health challenge in tropical and subtropical regions, with increasing incidence and mortality rates over the past few decades. Effective management, including early diagnosis and treatment, is crucial to reduce fatalities, especially in areas with limited healthcare infrastructure. From 1990 to 2019, global dengue cases rose significantly, with Southeast Asia and South Asia being the most affected regions [1] The age-standardized incidence rate (ASIR) increased by 3.16% annually, with the highest burden observed in high-middle and high sociodemographic index regions [2]. Accurate diagnosis of uncomplicated dengue requires clinical expertise and resources, which are often lacking in under-resourced areas [3] . The absence of effective vaccines complicates management, as the disease can escalate from mild to severe  \nforms, with a mortality rate of 20% in severe cases [4] . Current surveillance systems often underreport cases, necessitating improved integration of entomological and disease data t","cbCaiprR38a1q8LV","https://ap.wps.com/l/cbCaiprR38a1q8LV","pdf",3881230,9,1,10,"English","en",105,"# Introduction\n## Dengue epidemiology and diagnosis challenges\n## Limitations of traditional rule-based expert systems\n## Need for adaptive inference engines","[{\"question\":\"Why are traditional rule-based expert systems limited for dengue diagnosis?\",\"answer\":\"They rely on deterministic, predefined rules and expert-defined structures, making them less adaptable to changing knowledge and new patterns. As rules grow, maintenance becomes difficult and contradictions can increase.\"},{\"question\":\"How does the proposed approach improve expert systems for dengue disease?\",\"answer\":\"It integrates machine learning techniques into the inference engine so the system can learn dynamically from medical record data. This reduces dependence on static rule bases and allows the expert system to evolve with new knowledge.\"},{\"question\":\"Which machine learning models performed best and what were the reported metrics?\",\"answer\":\"Decision Trees, SVM, and ANN showed exceptional performance, with average accuracy, precision, recall, and F1 scores of 97.73%, 98.33%, 97.22%, and 97.41%, respectively.\"}]","Comparison of Machine Learning as an Inference Engine to Improve Expert Systems in Dengue Disease | PDF",1785904358,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"comparison-of-machine-learning-as-an-inference-engine-to-improve-expert-systems-in-dengue-disease","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/comparison-of-machine-learning-as-an-inference-engine-to-improve-expert-systems-in-dengue-disease/126303/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-21","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why are traditional rule-based expert systems limited for dengue diagnosis?","Question",{"text":77,"@type":78},"They rely on deterministic, predefined rules and expert-defined structures, making them less adaptable to changing knowledge and new patterns. As rules grow, maintenance becomes difficult and contradictions can increase.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the proposed approach improve expert systems for dengue disease?",{"text":82,"@type":78},"It integrates machine learning techniques into the inference engine so the system can learn dynamically from medical record data. This reduces dependence on static rule bases and allows the expert system to evolve with new knowledge.",{"name":84,"@type":75,"acceptedAnswer":85},"Which machine learning models performed best and what were the reported metrics?",{"text":86,"@type":78},"Decision Trees, SVM, and ANN showed exceptional performance, with average accuracy, precision, recall, and F1 scores of 97.73%, 98.33%, 97.22%, and 97.41%, respectively.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,120,125,129,132,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":118,"slug":119},40,"healthcare",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":123,"slug":124},8,"Research & Report",30,"research-report",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":22,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":108,"slug":138},19,"General","general"]