[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118719-en":3,"doc-seo-118719-105":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},118719,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Detection of Dengue Disease Empowered with Fused Machine Learning","Dengue fever poses a serious health threat across both industrialized and resource-limited nations, including Pakistan, where early forecasting is essential to reduce risk and enable timely clinical action. The study presents a fused machine learning approach for dengue prediction, grounded in an Artificial Neural Network (ANN) and a Support Vector Machine (SVM) conceptual framework. Data from a government hospital in Lahore is split into 70% training and 30% testing, and cloud storage supports future use with real-time patient inputs. The proposed fused model reaches 96.19% accuracy, outperforming prior work.","Detection of Dengue Disease Empowered with  \nFused Machine Learning  \nMohammad Rustom Al Nasar College of Computer Information Technology (CCIT), Department of Information Technology Management, American University in the Emirates (AUE), Academic City – Dubai, UAE.  \n[mohammad.alnasar@aue.ae](mohammad.alnasar@aue.ae)  \nTayba Asgher Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan  \n[asghertayba@gmail.com](asghertayba@gmail.com)  \nTamer Mohamed Canadian University Dubai, Dubai, UAE  \n[tamer.mohamed@cud.ac.ae](tamer.mohamed@cud.ac.ae)  \nIftikhar Nasir Faculty of Computer Science & Information Technology, Superior University, Lahore, 54000, Pakistan.  \n[iftikharnaseer@gmail.com](iftikharnaseer@gmail.com)  \nMahmoud M. Al-Sakhnini School of Business, Skyline University College, University City Sharjah, 1797, Sharjah, UAE  \nFaculty of Computer and Information Technology, Al-Madinah International University 57100,Kuala Lumpur, Malaysia  \n[m.alsakhnini@gmail.com](m.alsakhnini@gmail.com)  \nNouh Sabri Elmitwally School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, United Kingdom  \n[Nouh.elmitwally@bcu.ac.uk](Nouh.elmitwally@bcu.ac.uk)  \nAbstract—Dengue fever is a life-threatening illness that affects both industrialized and poor nations, including Pakistan. It is necessary to forecast the illness at an early stage to avoid it. Machine Learning (ML) methods outperform other computer approaches in terms of illness prediction. The model utilized in this study to predict dengue fever is fused with machine learning. Artificial Neural Networks (ANN) and Support Vector Machine (SVM) provide the foundation of the conceptual framework. The datasets employed in these models have been collected from a government hospital in Lahore, Pakistan for diagnosing dengue fever (positive or negative). 70% of the statistics in the dataset are training data, whereas 30% are testing data. This fused model's membership functions explain whether a dengue diagnostic is positive or negative, which controls the model's output. A cloud storage system saves the fused model based on patients' real-time information for future use. The proposed model has a 96.19 % accuracy rate, which is much greater than earlier research.  \nKeywords— Dengue Fever (DF), Dengue Hemorrhagic Fever (DHF), Dengue Prediction, Prediction Fused Dengue Model (PFDM)  \nI. INTRODUCTION (HEADING 1)  \nPakistan is an emerging country with limited assets and a high population. The high population density makes it difficult to take high risks, predominance, action, and other problems associated with dengue fever. In the pre-vaccination epoch, an epidemic can have several periods, each of which arises ina particular season. Dengue virus is spread to animals, as well as human beings, by mosquitoes. Dengue virus is transmitted by mosquitoes of the family Aedes aegypti and transmitted by the bite of female parasites [1] . Despite the short lifespan of dengue mosquitoes, the average time for the dengue virus to spread is 4 to 10 days after the larvae emerge from the eggs [2-4] . Dengue fever is increasing due to the combined effect of high temperature and precipitation [5] . Symptoms of dengue fever include nausea, discomfort in the extremities and joints, severe headache, and vomiting [1, 6] . Dengue fever and headache are common primary symptoms of this disease, but they can worsen over time. There are many types  \nof dengue viruses, and four have been discovered so far: DENV1, DENV2, DENV3, and DENV4 [2] . According to the World Health Organization (WHO), 2.5 billion people worldwide are at risk of contracting dengue, except for Antarctica [1] .  \nIn recent years, Pakistan has seen many natural hazards and challenges, like water shortages, flooding, earthquakes, and terrorists, all of which have harmed the country's resources and put the entire public's health at risk. ƊF has also bec","cbCaiedyQtStXLZZ","https://ap.wps.com/l/cbCaiedyQtStXLZZ","pdf",948751,1,10,"English","en",105,"# Introduction\n## Dengue epidemiology and risk factors\n## Motivation for ML-based diagnosis","[{\"question\":\"Why is early dengue forecasting important in this study?\",\"answer\":\"Early forecasting helps avoid severe outcomes by detecting the illness at an initial stage, reducing risk when intervention can be planned sooner.\"},{\"question\":\"What machine learning methods form the core of the proposed fused model?\",\"answer\":\"The conceptual framework combines Artificial Neural Networks (ANN) and Support Vector Machine (SVM) within a fused learning approach for dengue prediction.\"},{\"question\":\"How is the dataset used for training and testing, and what performance was achieved?\",\"answer\":\"The dataset collected from a government hospital in Lahore is split into 70% training data and 30% testing data, and the fused model achieves 96.19% accuracy.\"}]","Detection of Dengue Disease Empowered with Fused Machine Learning | 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is early dengue forecasting important in this study?","Question",{"text":75,"@type":76},"Early forecasting helps avoid severe outcomes by detecting the illness at an initial stage, reducing risk when intervention can be planned sooner.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning methods form the core of the proposed fused model?",{"text":80,"@type":76},"The conceptual framework combines Artificial Neural Networks (ANN) and Support Vector Machine (SVM) within a fused learning approach for dengue prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the dataset used for training and testing, and what performance was achieved?",{"text":84,"@type":76},"The dataset collected from a government hospital in Lahore is split into 70% training data and 30% testing data, and the fused model achieves 96.19% 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