Parkinson’s Disease Stage Classification with Gait Analysis using Machine Learning Techniques and SMOTE-based Approach for Class Imbalance Problem

Balakrishnan, Aishwarya; Medikonda, Jeevan; Namboothiri, Pramod K; Natarajan, Manikandan

doi:10.1109/discover55800.2022.9974754

Cited by 3 publications

(1 citation statement)

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“…The proportion of PD observations that were not recognized was exceptionally low (less than 10%, or 0.5%), showing that the machine learning model was able to identify complex patterns in the data and aid in identification. In [101], researchers demonstrated that the Synthetic Minority Oversampling Technique (SMOTE) improves minority class detection in order to address the class imbalance issue in PD stage-wise segmentation. By measuring the differences between the samples that were generated and demonstrating the lack of replication or overlapping, the method was validated.…”

Section: Random Forestmentioning

confidence: 99%

A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

Dixit

Bohre²,

Singh

et al. 2023

Electronics

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Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an intuitive assessment scale because of the disease’s characteristics, which include loss of motor control and speech that can be utilized to detect and diagnose this disease. It is an illness that impacts both motion and non-motion functions. It takes years to develop and has a wide range of clinical symptoms and prognoses. Parkinson’s patients commonly display non-motor symptoms such as sleep problems, neurocognitive ailments, and cognitive impairment long before the diagnosis, even though scientists have been working to develop designs for diagnosing and categorizing the disease, only noticeable defects such as movement patterns, speech, or writing skills are offered in this paper. This article provides a thorough analysis of several AI-based ML and DL techniques used to diagnose PD and their influence on developing additional research directions. It follows the guidelines of Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This review also examines the current state of PD diagnosis and the potential applications of data-driven AI technology. It ends with a discussion of future developments, which aids in filling critical gaps in the current Parkinson’s study.

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Section: Random Forestmentioning

confidence: 99%