[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125345-en":3,"doc-seo-125345-105":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},125345,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",7,"Healthcare","Hybrid Deep and Machine Learning Framework for Predicting Alzheimer’s Disease","Dementia encompasses a range of age-related brain-function symptoms such as memory loss and impaired thinking, with Alzheimer’s disease identified as a major cause. Diagnosis remains difficult for clinicians, motivating support from imaging and analytics. This paper introduces a hybrid approach combining deep learning with traditional machine learning to predict early Alzheimer’s using MRI data. Using two Kaggle MRI datasets, MobileNet paired with KNN achieves the highest reported performance (accuracy, precision, recall, and F1-score of 0.96).","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 21 No. 10 (2025) |   \n[https://doi.org/10.3991/ijoe.v21i10.541](https://doi.org/10.3991/ijoe.v21i10.541)13  \nPAPER  \nHybrid Deep and Machine Learning Framework for Predicting Alzheimer’s Disease  \nRaed Alazaidah1 (*), Hamza Abuassi1, Mo’ath Alluwaici1 , Mowafaq Salem Alzboon2 , Mohammad Subhi  \nAl-Batah2 , Muhyeeddin Alqaraleh1   \n1Faculty of Information Technology, Zarqa University, Zarqa, Jordan  \n2Faculty of Information Technology, Jadara University, Irbid, Jordan  \n[razaidah@zu.edu.jo](razaidah@zu.edu.jo)  \nABSTRACT  \nDementia is term related to many symptoms regarding brain abilities for old people. These symptoms include losing memory and thinking abilities. There are many causes leading to dementia, such as vascular dementia, Parkinson’s disease, and also severe head injury. But one of the biggest reasons is Alzheimer’s disease. Diagnostic of Alzheimer’s is challenging for the psychiatrists. There are many ways to diagnostic Alzheimer’s from conducting tests for memory to thinking skills to being evaluated by a healthcare professional. Brain-imaging as MRI, can be used to diagnose Alzheimer’s dementia earlier. This paper proposes a hybrid model to predict Alzheimer’s early by combining different machine learning (ML) models with deep learning models. Many models in this hybrid are used to get the powerful from each model and increasing the accuracy and to overcome the shortage of other models if it exist. We use two datasets of MRI for the brain from Kaggle. The result shows some hybrid models achieved outstanding results, as MobileNet with KNN scores the highest accuracy of 0.96, precision of 0.96, recall of 0.96, and F1-score of 0.96. This suggests that KNN is highly effective in leveraging the MobileNet. These top classifiers from the hybrid models indicate that combining robust feature extractors such as MobileNet, InceptionV3, and VGG16 with effective ML algorithms such as KNN, MLP, and random forest (RF) provides the best results for Alzheimer’s disease prediction.  \nKEYWORDS  \nAlzheimer’s disease, dementia, deep-learning, machine-learning (ML), feature-extraction  \n1 INTRODUCTION  \nAlzheimer’s disease is getting concern from World Health Organization (WHO) as one of the most common diseases recently for affecting on the memory and abilities [1] . Diagnostic of Alzheimer’s is challenging for the psychiatrists. There are many ways to diagnostic Alzheimer’s from conducting tests for memory and thinking skills and magnetic resonance imaging (MRI) to being evaluated by a healthcare professional. MRI shows the changes in the brain that help in the diagnosis  \nAlazaidah, R., Abuassi, H., Alluwaici, M., Alzboon, M. S., Al-Batah, M. S., Alqaraleh, M. (2025). Hybrid Deep and Machine Learning Framework for Predicting Alzheimer’s Disease. International Journal of Online and Biomedical Engineering (iJOE), 21(10), pp. 109–127. [https://doi.org/10.3991/ijoe.v21i10.541](https://doi.org/10.3991/ijoe.v21i10.541)13 Article submitted 2024-12-27. Revision uploaded 2025-03-09. Final acceptance 2025-03-09.  \n© 2025 by the authors of this article. Published under CC-BY.  \niJOE | Vol. 21 No. 10 (2025) International Journal of Online and Biomedical Engineering (iJOE) 109  \nAlazaidah et al.  \nprocess [2] . ML techniques help us diagnose disease due to their ability to analyze complex data and analyze patterns in images [3–6] . They help us analyze brain images and diagnose several diseases.  \nThe theoretical framework of this paper includes a comprehensive review of existing literature related to the utilization of ML techniques in brain image analysis to predict Alzheimer’s disease. The study begins with a review of the scientific foundations of Alzheimer’s disease, focusing on the neurobiology and neurostructural and functional c","cbCaibTJjV0BnPZo","https://ap.wps.com/l/cbCaibTJjV0BnPZo","pdf",1035805,1,19,"English","en",105,"# Abstract\n# Introduction\n# Related Work","[{\"question\":\"Why is predicting Alzheimer’s disease challenging?\",\"answer\":\"Diagnosis is difficult for psychiatrists because dementia symptoms overlap and clinical assessment alone is not sufficient. The paper notes imaging and structured models can support earlier prediction.\"},{\"question\":\"What data and methods are used in the proposed framework?\",\"answer\":\"The study uses two MRI brain datasets from Kaggle and builds a hybrid system combining deep learning feature extractors with machine learning classifiers.\"},{\"question\":\"Which hybrid model achieved the best results?\",\"answer\":\"MobileNet with KNN reports the highest accuracy, precision, recall, and F1-score of 0.96, indicating strong effectiveness in leveraging MobileNet features.\"}]","Hybrid Deep and Machine Learning Framework for Predicting Alzheimer’s Disease | PDF",1785898323,48,{"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},"hybrid-deep-and-machine-learning-framework-for-predicting-alzheimers-disease","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/hybrid-deep-and-machine-learning-framework-for-predicting-alzheimers-disease/125345/",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},"Why is predicting Alzheimer’s disease challenging?","Question",{"text":75,"@type":76},"Diagnosis is difficult for psychiatrists because dementia symptoms overlap and clinical assessment alone is not sufficient. The paper notes imaging and structured models can support earlier prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and methods are used in the proposed framework?",{"text":80,"@type":76},"The study uses two MRI brain datasets from Kaggle and builds a hybrid system combining deep learning feature extractors with machine learning classifiers.",{"name":82,"@type":73,"acceptedAnswer":83},"Which hybrid model achieved the best results?",{"text":84,"@type":76},"MobileNet with KNN reports the highest accuracy, precision, recall, and F1-score of 0.96, indicating strong effectiveness in leveraging MobileNet features.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]