[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122908-en":3,"doc-seo-122908-105":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},122908,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",7,"Healthcare","Alzheimer’s Disease Prediction from Handwriting using Machine Learning Algorithms","Alzheimer’s disease is a neurodegenerative condition common in older adults, and while there is no cure, early diagnosis enables interventions that can manage and delay symptom progression. This thesis applies machine learning models—including logistic regression, random forest, and extreme gradient boosting—to predict Alzheimer’s disease using the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset. Two experiments compare performance using all 450 features versus 25 feature sets. Evaluation relies on accuracy, ROC curves, and AUC. Results show random forest with all features performs best (91.43% accuracy, AUC 0.9441).","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nAlzheimer's Disease Prediction from Handwriting using Machine Learning Algorithms  \nPermalink  \n[https://escholarship.org/uc/item/7mt2h559](https://escholarship.org/uc/item/7mt2h559)  \nAuthor  \nChen, Xinyue  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nAlzheimer’s Disease Prediction from Handwriting using Machine Learning Algorithms  \nA thesis submitted in partial satisfaction of the requirements for the degree Master of Science in Statistics  \nby  \nXinyue Chen  \n2024  \n© Copyright by Xinyue Chen 2024  \nABSTRACT OF THE THESIS  \nAlzheimer’s Disease Prediction from Handwriting  \nusing Machine Learning Algorithms  \nby  \nXinyue Chen  \nMaster of Science in Statistics  \nUniversity of California, Los Angeles, 2024  \nProfessor Yingnian Wu, Chair  \nAlzheimer’s disease is a type of neurodegenerative disease that is common among the elderly. Although there is no cure, early diagnosis allows for treatments that can manage and delay the symptoms. We will employ machine learning algorithms, such as logistic regression, random forest, and extreme gradient boosting, to predict Alzheimer’s disease in two experiments. In the first experiment, each model is applied to all 450 features. In the second experiment, each model is applied to 25 different feature sets, with one set corresponding to each task. Predictions are based on the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset, and model performance is measured using accuracy, ROC curves, and AUC. The results indicate that the random forest model applied to all 450 features is the best performing model in predicting Alzheimer’s disease, achieving a model accuracy of 91.43% and an AUC of 0 .9441.  \nThe thesis of Xinyue Chen is approved.  \nHongquan Xu Mark S. Handcock Yingnian Wu, Committee Chair  \nUniversity of California, Los Angeles 2024  \nTo my family and Bagel, for their boundless love and support  \niv  \nTABLE OF CONTENTS  \n1 Introduction ...................................... 1  \n2 Data .......................................... 3  \n2.1 Data Overview .................................. 3  \n2.2 Data Cleaning ................................... 6  \n2.3 Exploratory Data Analysis ............................ 6  \n3 Methods ........................................ 12  \n3.1 Logistic Regression ................................ 12  \n3.2 Random Forest .................................. 16  \n3.3 Extreme Gradient Boosting ........................... 19  \n4 Results ......................................... 23  \n5 Conclusion and Future Work ........................... 27  \nReferences ......................................... 29  \nLIST OF FIGURES  \n2.1 Distribution of groups ................................ 7  \n2.2 Heat map of total time ................................ 9  \n2.3 Histograms of the distribution of total time .................... 10  \n2.4 Histograms of the distribution of disp index .................... 11  \n3.1 ROC curves for logistic regression on 25 tasks ................... 15  \n3.2 ROC curves for random forest on 25 tasks ..................... 18  \n3.3 ROC curves for XGBoost on 25 tasks ........................ 21  \n4.1 ROC curve of methods applied to all 450 features ................. 24  \nLIST OF TABLES  \n2.1 First 6 rows of the raw data ............................. 3  \n2.2 List of 25 tasks .................................... 4  \n2.3 Mean of total time .................................. 8  \n3.1 Accuracy (in percentage) achieved by logistic regression on each task ...... 14  \n3.2 AUC for logistic regression on 25 tasks ....................... 16  \n3.3 Accuracy (in percentage) achieved by random forest on each task ........ 17  \n3.4 AUC for random forest on 25 tasks ......................... 19  \n3.5 Accuracy (in percentage) achieved by XGBoost on each task ....","cbCaiqTIQj3oFW3G","https://ap.wps.com/l/cbCaiqTIQj3oFW3G","pdf",902416,1,39,"English","en",105,"# Introduction\n# Data\n## Data Overview\n## Data Cleaning\n## Exploratory Data Analysis\n# Methods\n## Logistic Regression\n## Random Forest\n## Extreme Gradient Boosting\n# Results\n# Conclusion and Future Work","[{\"question\":\"Why is early diagnosis of Alzheimer’s disease important in this thesis?\",\"answer\":\"Early diagnosis supports treatments that can manage and delay symptoms, helping slow disease progression even though there is no cure.\"},{\"question\":\"Which dataset is used to build the prediction models?\",\"answer\":\"The models are trained and evaluated using the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset.\"},{\"question\":\"How is model performance measured and what is the best-performing approach?\",\"answer\":\"Performance is assessed using accuracy, ROC curves, and AUC. The random forest model using all 450 features achieves the highest results (91.43% accuracy and AUC 0.9441).\"}]","Alzheimer’s Disease Prediction from Handwriting using Machine Learning Algorithms | PDF",1785813597,98,{"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},"alzheimers-disease-prediction-from-handwriting-using-machine-learning-algorithms","",{"@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/alzheimers-disease-prediction-from-handwriting-using-machine-learning-algorithms/122908/",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},"Why is early diagnosis of Alzheimer’s disease important in this thesis?","Question",{"text":75,"@type":76},"Early diagnosis supports treatments that can manage and delay symptoms, helping slow disease progression even though there is no cure.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset is used to build the prediction models?",{"text":80,"@type":76},"The models are trained and evaluated using the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance measured and what is the best-performing approach?",{"text":84,"@type":76},"Performance is assessed using accuracy, ROC curves, and AUC. 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