[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119566-en":3,"doc-seo-119566-105":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},119566,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Early Recognition of Parkinson’s Disease Through Acoustic Analysis and Machine Learning","Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that affects motor and non-motor functions, including speech. Early, accurate detection using speech-based acoustic analysis can support timely intervention and improved patient outcomes. This paper reviews methods for PD recognition from speech data, emphasizing machine-learning and data-driven workflows. It covers data collection, cleaning, transformation, and exploratory analysis, then evaluates models such as logistic regression, SVM, and neural networks with and without feature selection. Performance is assessed via accuracy, precision, and training time, with results indicating that discriminative acoustic features and advanced techniques separate PD cases from healthy controls. Model comparisons conclude the study and outline directions for future work.","arXiv :2407 . 16091v1 [math .NA] 22 Jul 2024  \nEarly Recognition of Parkinson’s Disease Through Acoustic Analysis and Machine Learning  \nNiloofar Fadavi [nfadavi@smu. edu](nfadavi@smu. edu)[ ](nfadavi@smu. edu)Southern Methodist University  \nNazanin Fadavi[naz. fadavi@aut. ac. ir](naz. fadavi@aut. ac. ir)[ ](naz. fadavi@aut. ac. ir)Amirkabir University of Technology  \nJuly 24, 2024  \nAbstract  \nParkinson’s Disease (PD) is a progressive neurodegenerative disorder that significantly impacts both motor and non-motor functions, including speech. Early and accurate recognition of PD through speech analysis can greatly enhance patient outcomes by enabling timely intervention. This paper provides a comprehensive review of methods for PD recognition using speech data, highlighting advances in machine learning and data-driven approaches. We discuss the process of data wrangling, including data collection, cleaning, transformation, and exploratory data analysis, to prepare the dataset for machine learning applications. Various classification algorithms are explored, including logistic regression, SVM, and neural networks, with and without feature selection. Each method is evaluated based on accuracy, precision, and training time. Our findings indicate that specific acoustic features and advanced machine-learning techniques can effectively differentiate between individuals with PD and healthy controls. The study concludes with a comparison of the different models, identifying the most effective approaches for PD recognition, and suggesting potential directions for future research.  \n1 Introduction  \nPD is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, bradykinesia (slowness of movement), and postural instability. These symptoms result from the degeneration of dopamine-producing neurons in the substantia nigra region of the brain. While PD primarily affects movement, it can also lead to a range of non-motor symptoms, including speech and voice disorders. Speech impairment is a common non-motor symptom of PD, manifesting as changes in voice quality, pitch variability, articulation, and phonation. These alterations, often subtle in the early stages of the disease, can significantly impact an individual’s quality of life and communication abilities. Asa result, there is growing interest in leveraging speech analysis techniques for the early detection and monitoring of PD. Traditional methods for PD diagnosis rely on clinical assessment by movement disorder specialists, which may involve subjective evaluations of motor symptoms and neuropsychological testing. However, these methods can be time-consuming, expensive, and may not capture subtle changes in speech patterns indicative of early-stage PD. In recent years, advances in machine learning and datadriven approaches have paved the way for the development of computational tools for PD recognition using speech data. These methods aim to extract quantitative features from speech recordings and utilize machine learning algorithms to discriminate between individuals with PD and healthy controls.  \nThe paper [3] contributes to Parkinson’s disease detection by including the Parkinson’s Telemonitoring dataset in its benchmarks for hyperparameter optimization (HPO) . It evaluates various HPO methods like Bayesian optimization and evolutionary algorithms on this dataset, providing insights into effective techniques for optimizing neural network architectures and hyperparameters. The paper [4] explores the impact of interaction effects between clustering and prediction algorithms, including their application in medical diagnoses such as Parkinson’s disease recognition. It discusses the use of voice recordings to predict PD, where clustering corresponds to grouping voice recordings of the same individual, and prediction involves determining whether the patient has PD. The study finds that traditional crossvalidation techniques exhibit significant emp","cbCaiiI54xHMe3SK","https://ap.wps.com/l/cbCaiiI54xHMe3SK","pdf",472218,1,12,"English","en",105,"# Introduction\n## Parkinson’s Disease and Speech Impairment\n## Traditional Diagnosis and Limitations\n## Machine Learning Approaches for PD Recognition\n## Related Work and Benchmarking","[{\"question\":\"Why is early recognition of Parkinson’s Disease important?\",\"answer\":\"Early and accurate PD recognition can enable timely intervention and improve patient outcomes, since PD progresses and speech changes can occur subtly in early stages.\"},{\"question\":\"How does the paper support PD recognition using speech data?\",\"answer\":\"It reviews a pipeline that collects and cleans speech data, extracts acoustic features, and applies machine-learning classifiers to distinguish individuals with PD from healthy controls.\"},{\"question\":\"Which evaluation metrics are used to compare the models?\",\"answer\":\"Models are compared using accuracy, precision, and training time, along with the impact of feature selection.\"}]","Early Recognition of Parkinson’s Disease Through Acoustic Analysis and Machine Learning | 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