{"title":"TabNet unveils predictive insights: a deep learning approach for Parkinson’s disease prognosis","authors":"Tapan Kumar, R. L. Ujjwal","doi":"10.1007/s13198-024-02450-4","DOIUrl":null,"url":null,"abstract":"<p>Parkinson’s disease (PD) is a neurodegenerative disorder affecting movement, speech, and coordination. Early diagnosis and intervention are crucial for improving the quality of life for PD patients. This study aims to enhance early PD diagnosis and improve patient outcomes using a novel approach. We proposed a TabNet model to classify patients with PD based on voice recordings and other features. TabNet is a neural network architecture designed specifically for tabular data. We compared its performance with support vector machines (SVMs), random forests (RFs), and decision trees (DTs). The TabNet model outperformed these methods, achieving an F1 Score of 83.03%. This demonstrates the model’s potential for more accurate PD diagnosis, which could lead to better patient management and treatment strategies.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":"206 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of System Assurance Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13198-024-02450-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder affecting movement, speech, and coordination. Early diagnosis and intervention are crucial for improving the quality of life for PD patients. This study aims to enhance early PD diagnosis and improve patient outcomes using a novel approach. We proposed a TabNet model to classify patients with PD based on voice recordings and other features. TabNet is a neural network architecture designed specifically for tabular data. We compared its performance with support vector machines (SVMs), random forests (RFs), and decision trees (DTs). The TabNet model outperformed these methods, achieving an F1 Score of 83.03%. This demonstrates the model’s potential for more accurate PD diagnosis, which could lead to better patient management and treatment strategies.
期刊介绍：
This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems.
Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.
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