[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122958-en":3,"doc-seo-122958-105":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},122958,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Analyzing electroencephalograph signals for early Alzheimer’s disease detection - deep learning vs. traditional machine learning approaches","Progressive neurodegenerative Alzheimer’s disease requires early and accurate diagnosis to enable timely interventions and treatment planning. This paper conducts a systematic comparison between conventional machine learning and modern deep learning methods for early AD detection. Traditional pipelines using feature extraction with algorithms such as support vector machines, decision trees, and K-nearest neighbors are contrasted with deep models including multilayer perceptrons and convolutional neural networks that learn representations directly from EEG signals. Results indicate that deep learning performs especially well with sufficient data, supporting more precise early diagnosis. The work positions methodology selection as a key factor in clinical translation.","Analyzing electroencephalograph signals for early Alzheimer’s disease detection: deep learning vs. traditional machine learning  \napproaches  \nSachin M. Elgandelwar1, Vinayak Bairagi1, Shridevi S. Vasekar2, Aziz Nanthaamornphong3,  \nPriyanka Tupe-Waghmare4  \n1Department of Electronics and Telecommunication Engineering, AISSMS Institute of Information Technology, Pune, India 2Department of Electronics and Telecommunication Engineering, Pune Institute of Computer Technology, Pune, India 3College of Computing, Prince of Songkla University, Phuket Campus, Phuket, Thailand  \n4Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India  \n\n| Article history:\u003Cbr>Received Oct 15, 2023 Revised Jan 24, 2024 Accepted Jan 25, 2024 | Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machine learning methods and advanced deep learning strategies for early AD diagnosis. Conventional ML algorithms like support vector machines, decision trees, and K nearest neighbor have been extensively employed for AD diagnosis through relevant feature extraction from heterogeneous data sources. Conversely, deep learning techniques such as multilayer perceptron (MLP) and convolutional neural networks (CNNs) have demonstrated exceptional aptitude in autonomously uncovering intricate patterns and representations from unprocessed data like EEG data. The findings reveal that while traditional ML methods may perform adequately with limited data, deep learning techniques excel when ample data is available, showcasing their potential for early and precise AD diagnosis. In conclusion, this research paper contributes to the ongoing discourse surrounding the choice of appropriate methodologies for early Alzheimer ’s disease diagnosis.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Alzheimer’s disease Deep learning\u003Cbr>Dementia Electroencephalograph signal Machine learning Neurodegenerative |  |\n\nCorresponding Author:  \nAziz Nanthaamornphong  \nCollege of Computing, Prince of Songkla University, Phuket Campus Phuket 83120, Thailand  \nEmail: [aziz.n@phuket.psu.ac.th](aziz.n@phuket.psu.ac.th)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn the field of medical diagnostics, there has been a significant focus on the early and accurate detection of Alzheimer disease (AD) through extensive research and innovation. This pursuit has given rise to two closely related and prominent areas of study: deep learning (DL) and machine learning (ML) . These approaches offer unique methodologies for analyzing intricate patterns within complex datasets and have shown great potential in facilitating the early diagnosis of AD [1], [2] . Machine learning, which falls under the umbrella of artificial intelligence, encompasses a variety of algorithms that enable computers to learn from data without explicit programming. This technique has been effectively utilized to uncover complex  \nrelationships within medical datasets, enabling the identification of subtle markers that indicate the presence of AD in its initial stages. Conversely, deep learning, a specialized subset of machine learning, utilizes neural networks like human brain structure. Its capacity to automatically extract hierarchical features from raw data has led to significant advancements in the analysis of medical bio signals. This has facilitated the detection of subtle anomalies that might not be discernible through traditional method [3], [4] .  \nIn this comparative exploration, the strengths and limitations of both DL and ML","cbCailEcRNbVVC1u","https://ap.wps.com/l/cbCailEcRNbVVC1u","pdf",1640900,1,14,"English","en",105,"# Abstract\n# Introduction\n## Machine learning for early AD detection\n## Deep learning for EEG-based analysis\n## Comparative challenges and clinical translation\n# Related work and references","[{\"question\":\"What is the main goal of the study on early Alzheimer’s detection?\",\"answer\":\"To compare deep learning and traditional machine learning approaches for diagnosing early Alzheimer’s disease using EEG-related data and feature analysis.\"},{\"question\":\"How do traditional machine learning methods used here typically work?\",\"answer\":\"They rely on extracting features from heterogeneous data sources and then training conventional algorithms such as support vector machines, decision trees, and k-nearest neighbors for AD diagnosis.\"},{\"question\":\"Why can deep learning be more effective when sufficient data is available?\",\"answer\":\"Deep learning models such as multilayer perceptrons and convolutional neural networks can automatically learn complex patterns and hierarchical representations from raw EEG data, which improves performance given enough training data.\"}]","Analyzing electroencephalograph signals for early Alzheimer’s disease detection - deep learning vs. traditional machine learning approaches | PDF",1785813892,35,{"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},"analyzing-electroencephalograph-signals-for-early-alzheimers-disease-detection-deep-learning-vs-traditional-machine-learning-approaches","",{"@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/analyzing-electroencephalograph-signals-for-early-alzheimers-disease-detection-deep-learning-vs-traditional-machine-learning-approaches/122958/",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","2026-08-04",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 is the main goal of the study on early Alzheimer’s detection?","Question",{"text":75,"@type":76},"To compare deep learning and traditional machine learning approaches for diagnosing early Alzheimer’s disease using EEG-related data and feature analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do traditional machine learning methods used here typically work?",{"text":80,"@type":76},"They rely on extracting features from heterogeneous data sources and then training conventional algorithms such as support vector machines, decision trees, and k-nearest neighbors for AD diagnosis.",{"name":82,"@type":73,"acceptedAnswer":83},"Why can deep learning be more effective when sufficient data is available?",{"text":84,"@type":76},"Deep learning models such as multilayer perceptrons and convolutional neural networks can automatically learn complex patterns and hierarchical representations from raw EEG data, which improves performance given enough training data.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]