Early diagnosis of Parkinson’s disease using machine learning algorithms

Şentürk, Zehra Karapınar

doi:10.1016/j.mehy.2020.109603

Cited by 185 publications

(67 citation statements)

References 18 publications

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“…In [41], although the authors obtained high accuracy, the KWELM model used in classification has high complexity and the AABC feature selection algorithm. In [39], the use of the RFE feature selection algorithm gives great accuracy compared to the results of [27], which are based on the whole 22 dataset features.…”

Section: Features Selection Resultsmentioning

confidence: 99%

A Novel Approach for Parkinson’s Disease Detection Based on Voice Classification and Features Selection Techniques

et al. 2021

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Parkinson’s disease (PD) is one of the most widespread diseases that, primarily, affects the motor system of the neural central system. In fact, PD is characterized by tremors, stiffness of the muscles, imprecise gait movements, and vocal impairment. An accurate diagnosis of Parkinson’s disease is usually based on many neurological, psychological, and physical investigations despite the fact that its main symptoms cannot be easily decorrelated from other diseases. As such, many automatic diagnostic support systems based on Machine Learning approaches have been recently employed to assist the PD patients' assessment. In the current paper, a comparative analysis was performed on machine learning (ML) techniques for PD identification based on voice disorders analysis. These ML methods included the Support Vector Machine (SVM), K-Nearest-Neighbors (KNN), and Decision Tree (DT) algorithms. In addition, two feature selection techniques; mRMR and ReliefF; are used to further improve the performance of the proposed classifiers. The efficiency of the developed model has been evaluated based on accuracy, sensitivity, specificity and AUC metrics, and it is higher than existing approaches. The simulation results show that the KNN algorithm yielded the best classifier performance in term of accuracy and reached an AUC of 98.26%.

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Section: Features Selection Resultsmentioning

confidence: 99%

A Novel Approach for Parkinson’s Disease Detection Based on Voice Classification and Features Selection Techniques

et al. 2021

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“…The neurological/psychiatric (neuropsychiatry) category comprises organically conditioned cognitive and mental disorders and is thus overlaps with the medical research fields of psychiatry, neurology, and psychology [147]. The classic neuropsychiatric diseases with symptoms in both the neurological and psychiatric fields are Parkinson's [148], dementia [55], and autism [149]…”

Section: Metabolicmentioning

confidence: 99%

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

2021

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The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.

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“…The algorithm calculates a rank score and eliminates the lowest-ranking features. Previous studies showed significant performance improvements by employing RFE, including predicting mental states (brain activity) [31,32], Parkinson [33], skin disease [34], autism [35], Alzheimer [36], and T2D [37]. They showed that SVM-RFE achieved superior performance than several comparison methods.…”

Section: Recursive Feature Eliminationmentioning

confidence: 99%

Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

et al. 2020

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Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.

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Early diagnosis of Parkinson’s disease using machine learning algorithms

Cited by 185 publications

References 18 publications

A Novel Approach for Parkinson’s Disease Detection Based on Voice Classification and Features Selection Techniques

A Novel Approach for Parkinson’s Disease Detection Based on Voice Classification and Features Selection Techniques

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

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