S.I. : ML4BD_SHS Automated methods for diagnosis of Parkinson’s disease and predicting severity level Zainab Ayaz 1 • Saeeda Naz 1 • Naila Habib Khan 2 • Imran Razzak 3 • Muhammad Imran 4 Received: 23 January 2021 / Accepted: 6 October 2021 Ó The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 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. Keywords Artificial intelligence Á Diagnosis Á Overview Á Parkinson’s disease 1 Introduction The rapid developments and advancements have revolu- tionized the recent era in data analytics, machine learning, artificial intelligence, the Internet of Things (IoT), and the exponential growth of computing power. Machine learning and data analytics play a huge role in healthcare, changing the diagnosis and treatment of various diseases. Likewise, artificial intelligence (AI) and machine learning (ML) have proven their worth and significance in medical sciences and pharmacy. Several expert systems and medical diagnostics applications have been developed to improve the patient’s health and life and assist the doctors and physicians’ expertise, skills, and practices. Accordingly, an excessive amount is being spent developing decision-making appli- cations, clinical tools, and machines. These applications, tools, and machines support doctors, psychologists, medi- cal specialists, and radiologists in the swift, accurate and timely detection of diseases [151]. Worldwide, a large number of people are suffering from diverse disorders such as depression, mental stress, anxiety, and diseases of the central nervous system such as Alz- heimer’s [185], dysgraphia [154], Parkinson’s disease, traumatic injury, vascular disease, disease of Lewy body and degeneration of frontotemporal lobar. The second most common and most frequent neurological disorder, known as motor system disorder, is Parkinson’s disease (PD). The diagnosis and detection of PD are tricky and challenging, and it is still an open and hot problem for researchers. The & Imran Razzak mirazzak@deakin.edu.au Naila Habib Khan naila.habib@icp.edu.pk 1 Computer Science Department, GGPGC No. 1, Abbottabad, KPK, Pakistan 2 Department of Computer Science, Islamia College Peshawar (Chartered University), Peshawar, KPK, Pakistan 3 School of Information Technology, Deakin University, Geelong, Australia 4 School of Engineering, Information Technology & Physical Sciences, Federation University, Brisbane 4000, Australia 123 Neural Computing and Applications https://doi.org/10.1007/s00521-021-06626-y