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A comparative study: prediction of parkinson’s disease using machine learning, deep learning and nature inspired algorithm

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Abstract

Parkinson’s Disease (PD) is a degenerative and progressive neurological disorder worsens over time. This disease initially affects people over 55 years old. Patients with PD often exhibit a variety of non-motor and motor symptoms and are diagnosed based on those motor and non-motor symptoms as well as numerous clinical indicators. Advancement in medical science has produced medicines for many diseases but till now no significant remedies are discovered for Parkinson disease. It is very necessary to detect PD at early phase to take precautions accordingly to reduce its harmful impact and improve the patient’s life style to a considerable level. In this direction Artificial Intelligence (AI) based approaches have recently attracted many researchers to work accordingly as AI can handle vast amounts of data and generate accurate statistical predictions. Addressing this imperative, researchers have turned their focus toward Artificial Intelligence (AI) as a promising avenue. AI’s capacity to manage vast datasets and generate precise statistical predictions makes it an invaluable tool for PD detection. This article aims to provide a comprehensive survey and in-depth analysis of various AI-based approaches. Leveraging machine learning (ML), deep learning (DL), and meta-heuristic algorithms, these approaches contribute to the prediction of PD. Additionally, the article delves into current research directions. As the pursuit of advancements continues, the integration of AI holds promise in revolutionizing early detection methods and subsequently improving the lives of individuals grappling with Parkinson’s disease.

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Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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    Pankaj Kumar Keserwani, Suman Das & Nairita Sarkar

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Keserwani, P.K., Das, S. & Sarkar, N. A comparative study: prediction of parkinson’s disease using machine learning, deep learning and nature inspired algorithm. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18186-z

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