Improving Parkinson's Disease Diagnosis with Machine Learning Methods

Çelik, Enes; Omurca, Sevinç İlhan

doi:10.1109/ebbt.2019.8742057

Cited by 35 publications

(11 citation statements)

References 14 publications

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“…Of these studies, 3 have reported the AUC as the performance metric (Du et al, 2017;Trezzi et al, 2017), leading to an average of 0.93 (0.08) and a range between 0.833 (Trezzi et al, 2017) and 0.997 . Among the 27 studies that compared regression with other machine learning models, regression achieved the highest performance in 3 studies, yielding an accuracy of 70% or 76.03% (Celik and Omurca, 2019), and an averaged AUC of 0.835 (Stoessel et al, 2018).…”

Section: Regression (N = 31)mentioning

confidence: 99%

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Mei

Desrosiers

Frasnelli

2021

Front. Aging Neurosci.

210

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Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

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Section: Regression (N = 31)mentioning

confidence: 99%

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Mei

Desrosiers

Frasnelli

2021

Front. Aging Neurosci.

210

View full text Add to dashboard Cite

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“…Sevinc Ilhan Omurca et., al. [8], proposed to cure Parkinson's disease several measures have been taken place to overcome the disease. For this several algorithms have been used to predict the better result.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor

Bala¹,

Anusuya²

2020

IJRTE

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Generally, the diseases are classified into communicable and non-communicable. The communicable disease is that, which can be spread easily from humans to humans while non-communicable disease does not spread. In this paper, we discuss about Parkinson's disease and its analysis using machine learning algorithms. The analysis of data is done using supervised machine learning approach. This paper concentrates and briefs about various supervised learning algorithms and its analysis.

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“…To predict PD, Cleick et al [ 61 ] presented a variety of classification methods, including Regression analysis, Support Vector Machine, Extra Trees, Gradient Boosting, and Random Forest ( Figure 10 ). In the classification stage, a total of 1208 voice data sizes were employed, with 26 features gathered from PD patients and non-patients.…”

Section: Artificial Intelligence Architecturesmentioning

confidence: 99%

“…According to the mean value, absolute score, and cumulative score of the concerned studies, the ranking of the studies was finalized. The ranking studies are mentioned in Table 2 [ 61 , 62 ]. The green, yellow, and red flags indicate the impact of low-bias, moderate-bias, and high-bias on individual cluster cells.…”

Section: Ranking Of Selected Studiesmentioning

confidence: 99%

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

et al. 2022

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Background and Motivation: Diagnosis of Parkinson’s disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. Method: The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. Result: The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were “deep learning with sketches as outcomes” and “machine learning with Electroencephalography,” respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. Conclusion: The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.

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Improving Parkinson's Disease Diagnosis with Machine Learning Methods

Cited by 35 publications

References 14 publications

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

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