[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124031-en":3,"doc-seo-124031-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124031,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Improving Performance Classification in Wireless Body Area Sensor Networks Based on Machine Learning - Techniques","Wireless Body Area Sensor Networks (WBASNs) have attracted significant attention in smart healthcare because they rely on timely, reliable delivery of vital physiological data. Data loss and delay cannot be tolerated when monitoring critical patient conditions. Machine learning enables analysis of collected sensor data to discover patterns, and to diagnose while notifying clinicians of emergencies. This study applies supervised classifiers—Learning Vector Quantization (LVQ) and Support Vector Machine (SVM)—to categorize incoming packets as normal, critical, or very critical, improving search performance and accuracy in heterogeneous WBASNs.","Improving Performance Classification in Wireless Body Area Sensor Networks Based on Machine Learning  \nTechniques  \nSabreen Waheed Kadhum* , Mohammed Ali Tawfeeq  \nComputer Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq  \n*Email: [sabreenwhd@uomustansiriyah.edu.iq](sabreenwhd@uomustansiriyah.edu.iq)  \n\n|  Article Info  |  |  Abstract \u003Cbr>Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two supervised machine learning classification techniques, Learning Vector Quantization (LVQ) and Support Vector Machine (SVM) classifiers, to achieve better search performance and high classification accuracy in a heterogeneous WBASN. These classification techniques are responsible for categorizing each incoming packet into normal, critical, or very critical, depending on the patient's condition, so that any problem affecting him can be addressed promptly. Comparative analyses reveal that LVQ outperforms SVM in terms of accuracy at 91.45% and 80%, respectively. |\n| --- | --- | --- |\n| Received\u003Cbr>Revised\u003Cbr>Accepted | 04/02/2024\u003Cbr>30/11/2024\u003Cbr>01/12/2024 |  |\n| Keywords: Data analytics, Learning Vector Quantization, Machine Learning, Support Vector Machine, Wireless Body Area Network |  |  |\n\n1. Introduction  \nNowadays, Smart healthcare is a highly dynamic and demanding field. Wireless Body Area Sensor Networks (WBASNs) play an essential role in healthcare monitoring since they utilize wireless sensors to monitor physiological data and anticipate the beginning of illnesses. [1],[2] . The most common applications of machine learning (ML) techniques play an important role in various fields, such as healthcare, childcare, and detecting emergencies [3] . Over the last decade, a large number of studies have utilized machine learning (ML) algorithms, as they are one of the main methodologies that clinical researchers are interested in. These techniques implement various markers to detect and categorize physiological information. Each of these researches has involved a different technique of ML and a special set of medical features to identify illnesses and conditions. Researchers have also identified and classified diseases using deep learning as a supervised learning technique in ML.  \nClassification involves gathering provided physiological information and creating a system that categorizes vital data into several distinct cases. [4] . WBASN is a cutting-edge medical system that helps monitor patients' vital signs. The aggregated physiological data is transmitted to the healthcare center for further processing [5] . Constructing an effective system for identifying huge vital data using ML on WBASNs is essential. This profoundly impacts the comprehensive examination of evaluating the generated physiological data of patients. through the implementation of WBASNs[6] .  \nData categorization is essential in mitigating network delay but also results in higher consumption power for a sensor when delivering many packets across the network [7] . The primary goal of this project is to develop a mechanism for segregating and classifying data in WBASNs to improve the overall performance of the network and to ensure a rapid response to critical and very critical situations. This will make the network more efficient, dependable, and able to supp","cbCaiiJbtOLtEa3H","https://ap.wps.com/l/cbCaiiJbtOLtEa3H","pdf",682109,1,"English","en",105,"# Introduction\n## Smart healthcare and WBASNs\n## Classification goals and performance trade-offs\n## Learning Vector Quantization (LVQ) overview\n## Data sources and feature selection\n# Proposed approach and classification scheme","[{\"question\":\"What classification problem does the study address in WBASNs?\",\"answer\":\"The study categorizes each incoming packet into normal, critical, or very critical states based on the patient’s condition to support prompt response.\"},{\"question\":\"Which supervised machine learning methods are used, and what is their role?\",\"answer\":\"Learning Vector Quantization (LVQ) and Support Vector Machine (SVM) are used as supervised classifiers to label packets according to health status categories.\"},{\"question\":\"What performance results does the study report when comparing LVQ and SVM?\",\"answer\":\"Comparative analysis shows LVQ achieves higher accuracy (91.45%) than SVM (80%) for the classification task.\"}]","Improving Performance Classification in Wireless Body Area Sensor Networks Based on Machine Learning - 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