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Automated methods for diagnosis of Parkinson’s disease and predicting severity level

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Abstract

The recent advancements in information technology and bioinformatics have led to exceptional contributions in medical sciences. Extensive developments have been recorded for digital devices, thermometers, digital equipments and health monitoring systems for the automated disease diagnosis of different diseases. These automated systems assist doctors with accurate and efficient disease diagnosis. Parkinson’s disease is a neurodegenerative disorder that affects the nervous system. Over the years, numerous efforts have been reported for the efficient automatic detection of Parkinson’s disease. Different datasets including voice data samples, radiology images, and handwriting samples and gait specimens have been used for analysis and detection. Techniques such as machine learning and deep learning have been used broadly and reported promising results. This review paper aims to provide a comprehensive survey of the use of artificial intelligence for Parkinson’s disease diagnosis. The available datasets and their various properties are discussed in detail. Further, a thorough overview is provided for the existing algorithms, methods and approaches utilizing different datasets. Several key peculiarities and challenges are also provided based on the comprehensive literature review to diagnose a healthy or unhealthy person.

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  1. Computer Science Department, GGPGC No. 1, Abbottabad, KPK, Pakistan

    Zainab Ayaz & Saeeda Naz

  2. Department of Computer Science, Islamia College Peshawar (Chartered University), Peshawar, KPK, Pakistan

    Naila Habib Khan

  3. School of Information Technology, Deakin University, Geelong, Australia

    Imran Razzak

  4. School of Engineering, Information Technology & Physical Sciences, Federation University, Brisbane, 4000, Australia

    Muhammad Imran

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Ayaz, Z., Naz, S., Khan, N.H. et al. Automated methods for diagnosis of Parkinson’s disease and predicting severity level. Neural Comput & Applic 35, 14499–14534 (2023). https://doi.org/10.1007/s00521-021-06626-y

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