[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118533-en":3,"doc-seo-118533-105":30,"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":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},118533,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Using Machine Learning Models to Predict Alzheimer’s Dementia","Alzheimer’s Disease remains one of the leading causes of death, with limited treatment options and significant global economic impact. Early diagnosis enables preventative care, and brain biomarkers can support staging through magnetic resonance imaging (MRI). A team developed multiple machine learning models using the ADNI dataset to classify patients’ Alzheimer’s stage, evaluating biomarker signals and testing performance through a convolutional neural network (CNN) approach.","Author(s): Eric Nguyen, Ada Duong, & Aristotelis Coskinas  \nTitle: Using Machine Learning Models to Predict Alzheimer’s Dementia  \nTerm: Summer 2023  \nDepartment: Business Administration  \nPurpose: Alzheimer’s Disease is one of the leading causes of death and its economic impact is pertinent on a global scale. With no effective cure, the disease is ultimately a race against time. Early diagnosis allows for preventative treatment and by understanding the biomarkers of the brain, we are able to pinpoint the stage of Alzheimer’s through magnetic resonance imaging (MRI) of the brain.  \nStudy design/methodology/approach: Our team created various machine learning models with the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to classify the patients’ stage of Alzheimer’s. Through studying the biomarkers and testing our data through the Convolutional Neural Network (CNN) model, we achieved the maximum accuracy with MRI image classification.  \nFindings: Our study found that the best model to approach large scale MRI classification is the CNN model. In addition, the atrophy of the brain is similar to a butterfly shape and though consistent, not all those afflicted with Alzheimer’s will have that shape in their MRI image. Though Alzheimer’s is the most common type of dementia, it is not the only cause. Hence, it is essential to run the images through a machine learning model for better accuracy and for a multifaceted result.  \nOriginality/value: Alzheimer’s Disease currently has no cure and the most effective method to slowing the progression of the disease is through early detection. Our application of the machine learning model to MRI images serves as a medium to link healthcare and technology. The incorporation of such models can assist physicians with prompt diagnosis and early detection.  \nPractical Implications: With the addition of machine learning models to Alzheimer’s care, clinics and hospitals can minimize costs and maximize accuracy. The accuracy achieved with machine learning models can allow patient MRI images to be classified efficiently and potentially reduce the diagnosis times for patients.  \nAristotelis Coskinas  \nI focused primarily on the research framework for this project and the two objectives that required content analysis. The biomarkers such as atrophy and shape of the ‘butterfly’ in the MRI images were one  \nof the main concerns we targeted in our research. Through my investigations, we resolved and identified the root cause and concluded that the image only serves as a reference point and is not unique to Alzheimer’s dementia only. Additionally, I drafted the structure of our final report and participated in choosing which model works best.  \nAda Duong  \nMy main contributions to this project include storytelling and presenting our findings. With no healthcare background, our team first focused on understanding the healthcare aspect of the project before proceeding with machine learning. I researched similar case studies relevant to this topic and identified the limitations to each one. From there, we were able to conduct a research project unique to us and after creating our models, I transformed our findings to presentations to tell a business story.  \nEric Nguyen  \nFor this project, I mainly worked on the last two objectives which are more coding focused. With our ADNI dataset, I first checked for nulls and cleaned the data as needed. Then I built the four models which include CNN, SVM, Boosted Learning Trees, and Logistic Regression. From the four models, I found that CNN is the best fit for our project due to its high accuracy and versatility with large datasets. From that model, we achieved a 99% accuracy with predicting MRI images.  \nKeywords: Alzheimer’s Disease, Convolutional Neural Network, MRI, Dementia, Prediction  \nCommittee Chair: Honggang Wang  \nCommittee Member(s): Hyounae (Kelly) Min","cbCaitcMtUGjAgSn","https://ap.wps.com/l/cbCaitcMtUGjAgSn","pdf",141401,1,2,"English","en",105,"# Study Purpose\n## Background and Need for Early Diagnosis\n# Study Design and Methodology\n## Dataset and Model Building\n## CNN-Based MRI Classification\n# Key Findings and Insights\n## Best-Performing Model\n## Biomarker Patterns and Limits\n# Practical Implications and Value\n## Clinical Efficiency and Cost Considerations","[{\"question\":\"How does the study enable early diagnosis for Alzheimer’s Disease?\",\"answer\":\"It uses brain biomarkers observable on MRI to pinpoint the Alzheimer’s stage, supporting earlier detection and more preventative care.\"},{\"question\":\"Which machine learning model performed best in MRI classification?\",\"answer\":\"The CNN model achieved the maximum accuracy for large-scale MRI classification.\"},{\"question\":\"What datasets and models were used to classify Alzheimer’s stages?\",\"answer\":\"The team used the ADNI dataset and built four models: CNN, SVM, Boosted Learning Trees, and Logistic Regression.\"}]","Using Machine Learning Models to Predict Alzheimer’s Dementia | 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does the study enable early diagnosis for Alzheimer’s Disease?","Question",{"text":74,"@type":75},"It uses brain biomarkers observable on MRI to pinpoint the Alzheimer’s stage, supporting earlier detection and more preventative care.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning model performed best in MRI classification?",{"text":79,"@type":75},"The CNN model achieved the maximum accuracy for large-scale MRI classification.",{"name":81,"@type":72,"acceptedAnswer":82},"What datasets and models were used to classify Alzheimer’s stages?",{"text":83,"@type":75},"The team used the ADNI dataset and built four models: CNN, SVM, Boosted Learning Trees, and Logistic 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