[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119439-en":3,"doc-seo-119439-105":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},119439,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Key Feature Analysis for Predicting Speech of People with Parkinson’s Disease in Machine Learning - Thesis","Parkinson’s Disease (PD) is addressed as a major public health challenge by focusing on automated prediction from speech signals using machine learning. The study emphasizes extracting and selecting key speech features that characterize voice and speech disorders associated with PD, then training multiple models to classify outcomes. Data is collected and explored, followed by systematic feature selection and hyperparameter optimization. Model performance is evaluated with testing on real-world collected data and comparative analysis across algorithms to support robust conclusions and directions for future work.","California State University Northridge  \nKey Feature Analysis for Predicting Speech of People with Parkinson’s Disease in  \nMachine Learning  \nA thesis submitted in partial fulfillment of the requirements For the degree of Master of Science in Computer Science  \nBy Ashfaq Hussain Mohammad  \nDecember 2023  \nThe thesis of Ashfaq Hussain Mohammad is approved:  \nProfessor Mahdi Ebrahimi Date  \nProfessor Vickie Yu  \nDate  \n__________________________________________ _______________  \nProfessor Li Liu, Chair Date  \nCalifornia State University, Northridge  \nAcknowledgement  \nI would like to express my heartfelt gratitude to Professor Li Liu and Professor Vickie Yu for their invaluable guidance, unwavering support, and insightful mentorship throughout the course of my thesis work. Your dedication to fostering intellectual curiosity and your commitment to excellence have been instrumental in shaping my academic journey. I am truly fortunate to have had the opportunity to learn from your expertise and benefit from your constructive feedback. Your encouragement and wisdom have been a constant source of inspiration, and I am deeply grateful for the knowledge and skills I have gained under your mentorship. I would also like to thank Professor Mahdi Ebrahimi for agreeing to join my thesis committee. Thank you for your enduring encouragement and for being exemplary educators and mentors.  \nTable of Contents  \nSignature Page…………………………………………………………………………….ii  \nAcknowledgement…………………………………………………………………….….iii  \nList of Tables……………………………………………………...………………………vi  \nList of Figures…………………………………………………………………………....vii  \nAbstract………………………………………………………………………………...…ix  \n1. Introduction ................................................................................................................. 1  \n1.1 Background ......................................................................................................... 1  \n1.2 Feature of Speech from People with Speech Disorder ....................................... 3  \n2. Literature Review........................................................................................................ 5  \n3. Research Methodology ............................................................................................... 9  \n3.1 Overview ............................................................................................................. 9  \n3.2 Data Collection ................................................................................................. 10  \n3.3 Data Exploration ............................................................................................... 15  \n3.4 Feature Selection............................................................................................... 16  \n3.5 Machine Learning Algorithms .......................................................................... 17  \n3.6 Performance Evaluation .................................................................................... 22  \n4. Results ....................................................................................................................... 27  \n4.1 Model Performance........................................................................................... 27  \n4.2 Hyperparameter Optimization Results .............................................................. 34  \n4.3 Testing the Models on Real-World Collected Data .......................................... 41  \n5. Conclusion ................................................................................................................. 43  \n6. Future Work................................................................................................................ 45  \nReferences ......................................................................................................................... 46  \nList of Tables  \nTable 1: Short Description of MDVP features…………………………………………..10  \nTable 2: Comparing Accuracies of ML models.……………………","cbCaisvWeTVIZ5EH","https://ap.wps.com/l/cbCaisvWeTVIZ5EH","pdf",3726912,1,58,"English","en",105,"# Introduction\n## Background\n## Feature of Speech from People with Speech Disorder\n# Literature Review\n# Research Methodology\n## Overview\n## Data Collection\n## Data Exploration\n## Feature Selection\n## Machine Learning Algorithms\n## Performance Evaluation\n# Results\n## Model Performance\n## Hyperparameter Optimization Results\n## Testing the Models on Real-World Collected Data\n# Conclusion\n# Future Work","[{\"question\":\"What is the main goal of this thesis?\",\"answer\":\"The thesis aims to analyze and select key speech features and use machine learning to predict speech outcomes related to Parkinson’s Disease.\"},{\"question\":\"How is the research process structured?\",\"answer\":\"It follows data collection and exploration, feature selection, training multiple machine learning algorithms, and performance evaluation with hyperparameter optimization.\"},{\"question\":\"What evaluation approach is used to compare models?\",\"answer\":\"Models are evaluated through performance metrics including classification reports and ROC curves, and their tuned versions are compared. Testing is also performed on real-world collected data.\"}]","Key Feature Analysis for Predicting Speech of People with Parkinson’s Disease in Machine Learning - Thesis | PDF",1785724293,146,{"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},"key-feature-analysis-for-predicting-speech-of-people-with-parkinsons-disease-in-machine-learning-thesis","",{"@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/key-feature-analysis-for-predicting-speech-of-people-with-parkinsons-disease-in-machine-learning-thesis/119439/",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","2026-08-03",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 this thesis?","Question",{"text":75,"@type":76},"The thesis aims to analyze and select key speech features and use machine learning to predict speech outcomes related to Parkinson’s Disease.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the research process structured?",{"text":80,"@type":76},"It follows data collection and exploration, feature selection, training multiple machine learning algorithms, and performance evaluation with hyperparameter optimization.",{"name":82,"@type":73,"acceptedAnswer":83},"What evaluation approach is used to compare models?",{"text":84,"@type":76},"Models are evaluated through performance metrics including classification reports and ROC curves, and their tuned versions are compared. 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