[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121286-en":3,"doc-seo-121286-105":30,"detail-sidebar-cat-0-en-105":83},{"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},121286,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",7,"Healthcare","An efficient Machine Learning Techniques for Early Detection of Hearing Loss","By 2050, over 700 million people are projected to experience severe hearing loss, while audiologists and otolaryngologists remain scarce in many low- and middle-income regions. Long-term untreated impairment stems from limited access to specialist care. This study introduces automated hearing impairment diagnosis software using machine learning to help clinicians consistently and effectively identify and classify hearing loss. A two-module architecture combines a Data Acquisition/Generation module for large hearing-test datasets with a machine learning model using multi-class and multi-label learning for real-time prediction.","An efficient Machine Learning Techniques for Early Detection of Hearing Loss  \nDr.Manjula Devarakonda Venkata 1 , Vudathu Venkata Naga Anjana Pravallika2, Baki Leela Kowshik Reddy3, Akasam Amulya Sai4, Marri Sujana5, Polamarasetti Prem Kumar6 , 1Associate Professor , 2,3,4,5, 6[B.tech](B.tech) Students  \nDepartment of Computer Science Engineering, Pragati Engineering College, Surampalem , Andhra Pradesh,  \nIndia  \nEmail: [dv.manjula@pragati.ac.in](dv.manjula@pragati.ac.in)  \nAbstract:  \nBy 2050, over 700 million people will have severe hearing loss. Audiologists and otolaryngologists are in short supply in underdeveloped and emerging countries, where a considerable part of the population suffers from incapacitating hearing loss. Most hearing impairments are untreated for long periods of time due to a scarcity of specialists. In this study, we present automated hearing impairment diagnosis software based on machine learning to help audiologists and otolaryngologists consistently and effectively identify and classify hearing loss.We discuss the architecture, implementation, and performance evaluation of the two-module automated program for diagnosing hearing impairments: a machine learning model and a module for creating hearing test data. To train and evaluate the machine learning model, the Data Acquisition Module generates a sizable and comprehensive dataset. The kind, degree, and arrangement of hearing loss can be accurately predicted by the model in real time using multiple classes and multi-label classification algorithms that learn from hearing test data.With a log loss reduction rate of 98.48%, a prediction time of 634 ms, and macro and micro precisions of 100%, our proposed machine learning model shows promise and can help audiologists and otolaryngologists quickly and accurately classify the type, degree, and configuration of hearing loss.  \nKey Words: Audiometry, hearing impairment, machine learning, multiclass classification, multi-label classification  \nI. Introduction  \nThe World Health Organization (WHO) estimates that by2050[11], approximately 2.5 billion people will suffer from hearingimpairment [1]. Of these individuals, 700 million willexperience disabling hearing loss—defined asan inability toperceive sound lower than 35 decibels (dB) [2] . Incapacitatinghearing loss diminishes the quality of life, learning opportunities,and chances for employment.Approximately 80% of people with disabling hearing impairmentlive in low- or middle-income countries [2] . Thesenations have insufficient hearing care infrastructure as part oftheir national health care systems. According to the WHO,78% of low-income countries possess fewer than one otolaryngologister million, and 93% have fewer than one audiologistper million [1] . This disparity between the hearing-impairedpatients and the health care infrastructure burdens the latter,which is unable to meet the demand of the patients. By facilitating the precise, quick, and effective detection of hearing impairment, automatic hearing diagnosis software that is being developed will help the overworked health care system. The second author's earlier open-source Audiometry application [3]–[8] is enhanced by the suggested software. The application Audiometry has the ability to save,process, and visualize data for tuning fork tests includingWeber, Teal, Schwabach, Rinne, Gelle, and absolute boneconduction; speech audiometry; biothermal caloric test, pure-tone audiometry (PTA), impedance audiometry, and advanced tests such as Stenger, tone decay, short increment sensitive index, and alternate binaural loudness balancing [9]–[11] . It does not, however, carry out automated tests to support the diagnosis of hearing impairment. This functionality will be added to the  \napplication via the proposed software. The design, deployment, and outcome analysis of an automatic hearing loss diagnosis software are covered in this study.The machine learning model and data generation are the two elemen","cbCaiqzP30pyklCB","https://ap.wps.com/l/cbCaiqzP30pyklCB","pdf",386859,1,8,"English","en",105,"# Introduction\n# Literature Survey\n# Methodology\n## Data Generation Module\n## Machine Learning Model\n# Performance Evaluation\n# Access, Execution, and Extension\n# Conclusion and Future Research Directions","[{\"question\":\"What classification approach is used by the machine learning model?\",\"answer\":\"The model employs multiple classes and multi-label classification algorithms trained on hearing test data generated by the data acquisition module.\"}]","An efficient Machine Learning Techniques for Early Detection of Hearing Loss | PDF",1785734914,20,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"an-efficient-machine-learning-techniques-for-early-detection-of-hearing-loss","",{"@graph":36,"@context":77},[37,54,68],{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-efficient-machine-learning-techniques-for-early-detection-of-hearing-loss/121286/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What classification approach is used by the machine learning model?","Question",{"text":75,"@type":76},"The model employs multiple classes and multi-label classification algorithms trained on hearing test data generated by the data acquisition module.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,110,114,118,121,125],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":108,"slug":109},40,"healthcare",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":29,"slug":117},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":29,"slug":120},"World Cup","world-cup",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":122,"slug":124},10,"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":98,"slug":128},19,"General","general"]