[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118922-en":3,"doc-seo-118922-105":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},118922,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",7,"Healthcare","Accident Severity Detection Using Machine Learning - Model Development and Web Portal","Road accident severity directly affects casualties, long-term disability, and substantial financial losses, creating pressure on hospitals and the wider healthcare system. The study develops a machine learning approach to classify accident outcomes into slight and serious categories by learning from historical accident records and relevant contributing factors. A gradient boosting model delivers about 90% accuracy. Experiments use publicly accessible European data and compare multiple classifiers, and the resulting model is deployed through an intelligent web portal for severity prediction.","Accident Severity Detection Using Machine Learning  \nNagma Bi 1 and Dr. Halima Sadia2  \n1Student, Department of Computer Science & Engineering, Integral University, INDIA 2Associate Professor, Department of Computer Science & Engineering, Integral University, INDIA  \n[1](1Corresponding Author: nagma1215@gmail.com)[Corresponding Author: nagma1215@gmail.com](1Corresponding Author: nagma1215@gmail.com)  \nReceived: 29-05-2023 Revised: 16-06-2023 Accepted: 30-06-2023  \nABSTRACT  \nRoad accidents are one of the most regrettable hazards in this hectic world. Each year, traffic accidents cause a large number of casualties, illnesses, and deaths in addition to suffering huge financial losses.There are numerous things that cause traffic accidents, especially those related to the environment, vehicles and the travelers.By analyzing the severity of the road accidents that happened in the past, and the factors that caused it, it is possible to take precautionary measures to reduce the road accidents rate significantly in the future.This project includes developing a machine learning model that can categorize accident severity depending on the circumstances that affected the accident. A prediction model was created using a variety of machine learning classifiers, including Gradient Boosting Classifiers (GBC), K-Nearest Neighbour (KNN), Random Forest (RF), and Decision Tree (DT). The severity of a road accident can be detected 90% accurately, according to the results of a gradient boosting algorithm.The study makes use of publicly accessible European data.The approach presented in the research is broad enough to be used with various data sets from other nations.Additionally, the web portal's model was used to create an intelligent system for predicting the severity of accidents.  \nKeywords—Decision tree (DT), K- Nearest Neighbour (KNN), Random Forest (RF), Gradient Boosting Classifiers (GBC)  \nI. INTRODUCTION  \nA road traffic accident (RTA) is an unanticipated incident that happens on the road and involves a vehicle and/or other road users and results in casualties or property loss.The economic and social levels are greatly impacted by traffic accidents. In 2030, it's predicted that road accidents would account for a significant portion of fatalities. Many people's lives and entire communities have improved thanks to motorization, however, there are costs associated with the advantages.Road fatalities account for more than 90% of all global deaths.  \nLow-income nations only make up 48% of the world's registered autos. More money was lost financially—about $518 billion—than was given to these nations in development aid. Developing nations continue to lose 13% of their gross national product (GNP) due to the epidemic of traffic fatalities, despite the affluent nations' stable or dropping road traffic death rates as a  \nresult of concerted corrective efforts from many sectors. Road crashes could become the sixth leading cause of death worldwide, according to the World Health Organization (WHO).Authors evaluate several severity levels, such as light injury and severe injury.Accidents area significant contributor to illnesses, fatalities, long-term disability, and property damage. Because it burdens the hospitals, it has an impact on both the economy and the healthcare system. Accident severity prediction, one of the key topics in accident management, is crucial to the rescuers' assessment of the seriousness of traffic accidents, their potential effects, and the implementation of effective accident management measures.The number of injuries, the number of fatalities, and the quantity of property damage are the three elements that make up an analysis of the severity of an accident. The prediction model looks into the relationships between different crash severity injury categories and factors that may have contributed to the accident, including driver behaviour, vehicle characteristics, road geometry, environmental factors, and accident causes.Ther","cbCaieU4NOkqiTPI","https://ap.wps.com/l/cbCaieU4NOkqiTPI","pdf",371370,1,6,"English","en",105,"# Introduction\n## Accident impact and severity factors\n# Methodology\n## Project overview and phases\n## Dataset description\n## Data preprocessing","[{\"question\":\"What problem does the project address?\",\"answer\":\"The project targets road accident severity prediction, classifying accidents into slight or serious categories to support effective accident management.\"},{\"question\":\"Which machine learning models are evaluated?\",\"answer\":\"It evaluates several classifiers including Gradient Boosting Classifiers (GBC), K-Nearest Neighbour (KNN), Random Forest (RF), and Decision Tree (DT).\"},{\"question\":\"How is the trained model used after evaluation?\",\"answer\":\"An intelligent web portal is developed to use the trained model for predicting accident severity based on input accident data.\"}]","Accident Severity Detection Using Machine Learning - 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