[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127164-en":3,"doc-seo-127164-105":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},127164,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Using Machine Learning for Recognition of Alzheimer’s Disease Based on Transcription Information","The article presents analytical and prognostic studies for recognizing Alzheimer’s disease using decoded text speech data and machine learning. Speech transcripts are sourced from the ADReSS 2020 Challenge, including recordings from patients and healthy controls. The task is framed as binary classification, where complete interview texts are extracted from transcriptions. A model is trained with vectorized text features using a random forest classifier, with hyperparameters optimized via GridSearchCV. The achieved classification accuracy is 85.2%.","[http://dx.doi.org/10.35596/1729-7648-2023-21-6-106-112](http://dx.doi.org/10.35596/1729-7648-2023-21-6-106-112)[ ](http://dx.doi.org/10.35596/1729-7648-2023-21-6-106-112)Original paper  \nUDC 004 .78; 615.47  \nUSING MACHINE LEARNING FOR RECOGNITION OF ALZHEIMER’S DISEASE BASED ON TRANSCRIPTION INFORMATION  \nULADZIMIRA. VISHNIAKOU, YU CHU YUE  \nBelarusian State University of Informatics and Radioelectronics (Minsk, Republic of Belarus)  \nSubmitted 12.07.2023  \n© Belarusian State University of Informatics and Radioelectronics, 2023 Белорусский государственный университет информатики и радиоэлектроники, 2023  \nAbstract. The purpose of this article is to perform analytical and prognostic studies on the recognition of Alzheimer’s disease based on decoded text speech data using machine learning algorithms. The data used in this article is taken from the ADReSS 2020 Challenge program, which contains speech data from patients with Alzheimer’s disease and healthy people. The problem under study is a binary classification problem. First, the full texts of the interviewees were extracted from the transcribed texts of the speech data. This was followed by training the model based on vectorized text features using a random forest classifier, in which the authors used the GridSearchCV method to optimize hyperparameters. The classification accuracy of the model reached 85.2 % .  \nKeywords: machine learning, random forest method, binary classification, optimization parameters.  \nConflict of interests. The authors declare no conflict of interests.  \nFor citation. Vishniakou U. A., Yu Chu Yue (2023) Using Machine Learning for Recognition of Alzheimer’s Disease Based on Transcription Information. Doklady BGUIR. 21 (6), 106–112. [http://dx.doi.org/10.35596/1729-](http://dx.doi.org/10.35596/1729-)[ ](http://dx.doi.org/10.35596/1729-)7648-2023-21-6-106-112.  \nИСПОЛЬЗОВАНИЕ МАШИННОГО ОБУЧЕНИЯДЛЯ РАСПОЗНАВАНИЯ БОЛЕЗНИ АЛЬЦГЕЙМЕРАНА ОСНОВЕ ТРАНСКРИПЦИОННОЙ ИНФОРМАЦИИ  \nВ . А . ВИШНЯКОВ, ЮЙ ЧУ ЮЭ  \nБелорусский государственный университет информатики и радиоэлектроники  \n(г. Минск, Республика Беларусь)  \nПоступила в редакцию 12.07.2023  \nАннотация. Выполнены аналитические и прогностические исследования по распознаванию болезниАльцгеймера на основе расшифрованных текстовых речевых данных с использованием алгоритмов машинного обучения. Данные были взяты из программы ADReSS 2020 Challenge, которая содержит речевыеданные пациентов с болезнью Альцгеймера и здоровых людей . Распознавание болезни Альцгеймера представляет собой проблему бинарной классификации . Сначала из расшифрованных текстов речевых данныхизвлекались полные тексты интервьюируемых пациентов . Затем следовало обучение модели нейроннойсети на основе векторизованных текстовых признаков с использованием классификатора случайного леса,в котором авторы применяли метод GridSearchCV для оптимизации гиперпараметров . Точность классификации модели составила 85,2 % .  \nКлючевые слова: машинное обучение, метод случайного леса, бинарная классификация, параметры оптимизации .  \nКонфликт интересов. Авторы заявляют об отсутствии конфликта интересов .  \nДля цитирования. Вишняков, Ю . А . Использование машинного обучения для распознавания болезниАльцгеймера на основе транскрипционной информации / Ю. А. Вишняков, Юй Чу Юэ // Доклады БГУИР.  \n2023. Т. 21, № 6. С. 106–112. [http://dx.doi.org/10.35596/1729-7648-2023-21-6-106-112](http://dx.doi.org/10.35596/1729-7648-2023-21-6-106-112) .  \nIntroduction  \nAlzheimer’s disease (AD) is an insidious and progressive neurodegenerative disease, clinically defined as the impairment of certain cognitive and functional abilities [1]. Internationally, no medical treatment has been developed to cure AD, which causes progressive and irreversible damage to the patient, in all cases leading to neurological death [2]. Memory loss and language impairment are among the earliest symptoms of AD and have become the direction in which scholars are working to help Alzheimer’s patients tod","cbCaidqmCqRYtZfV","https://ap.wps.com/l/cbCaidqmCqRYtZfV","pdf",486565,1,7,"English","en",105,"# Introduction\n# Review\n# Methods\n# Results\n# Conflict of interests","[{\"question\":\"What data source is used for Alzheimer’s disease recognition in this study?\",\"answer\":\"The study uses speech data from the ADReSS 2020 Challenge program, containing transcripts from patients with Alzheimer’s disease and healthy people.\"},{\"question\":\"How is the recognition problem formulated?\",\"answer\":\"It is treated as a binary classification problem to distinguish Alzheimer’s patients from healthy controls.\"},{\"question\":\"Which model and optimization approach are used?\",\"answer\":\"The authors train a random forest classifier on vectorized text features and optimize hyperparameters using GridSearchCV.\"}]","Using Machine Learning for Recognition of Alzheimer’s Disease Based on Transcription Information | PDF",1785937269,18,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"using-machine-learning-for-recognition-of-alzheimers-disease-based-on-transcription-information","",{"@graph":36,"@context":85},[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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-machine-learning-for-recognition-of-alzheimers-disease-based-on-transcription-information/127164/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data source is used for Alzheimer’s disease recognition in this study?","Question",{"text":75,"@type":76},"The study uses speech data from the ADReSS 2020 Challenge program, containing transcripts from patients with Alzheimer’s disease and healthy people.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the recognition problem formulated?",{"text":80,"@type":76},"It is treated as a binary classification problem to distinguish Alzheimer’s patients from healthy controls.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model and optimization approach are used?",{"text":84,"@type":76},"The authors train a random forest classifier on vectorized text features and optimize hyperparameters using GridSearchCV.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]