Parkinson's disease classification using machine learning algorithms: performance analysis and comparison

Ouhmida, Asmae; Raihani, Abdelhadi; Cherradi, Bouchaib; Lamalem, Yasser

doi:10.1109/iraset52964.2022.9738264

Cited by 11 publications

(5 citation statements)

References 44 publications

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“…In this study, we trained and tested five different AI models using the same feature vectors that were extracted (using the AE model) from the 122 DBS-fMRI response maps (32,33). The performance of the AE-based MLP, SVM, RF, KNN and LDA classification models were accessed to facilitate comparison with our previously published parcel-based LDA implementation (11).…”

Section: Artificial Intelligence Methods For Dbs Parameters Settings ...mentioning

confidence: 99%

Autoencoder-based Feature Extraction and Classification for fMRI-based Deep Brain Stimulation Parameter Optimization for Parkinson’s Disease Treatment: Towards a Rapid Semi-automated Stimulation Optimization

Ajala,

Qiu,

Karigiannis

et al. 2024

Preprint

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Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the post-operative optimization (patient clinical benefits are maximized and adverse effects are minimized) of the large number of possible DBS parameter settings (signal frequency, voltage, pulse width and contact locations) using the current empirical protocol requires numerous clinical visits, which substantially increases the time to reach optimal DBS stimulation, patient cost burden and ultimately limits the number of patients who can undergo DBS treatment. These issues became even more problematic with the recent introduction of electrode models with stimulation directionality thereby enabling more complex stimulation paradigms. These difficulties have necessitated the search for a biomarker-based optimization method that will streamline the DBS optimization process. Our recently published functional magnetic resonance imaging (fMRI) and machine learning-assisted DBS parameter optimization for PD treatment has provided a way to rapidly classify DBS parameters using parcel-based features that were extracted from DBS-fMRI response maps. However, the parcel-based method had limited accuracy as the parcels are based on subjective literature review. Here, we propose an unsupervised autoencoder (AE) based extraction of features from the DBS-fMRI responses to improve this accuracy. We demonstrate the usage of the extracted features in classification methods such as multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN) and LDA. We trained and tested these five classification algorithms using 122 fMRI response maps of 39 PD patients with a priori clinically optimized DBS parameters. Further, we investigated the robustness of the AE-based feature extraction method to changes in the activation patterns of the DBS-fMRI responses, which may be caused by difference in stimulation side and disease condition. Changes in the locations of activated and deactivated brain regions was simulated using a left-right horizontal flipping of the original left-sided (or nominal) DBS-fMRI response maps. The visualization of AE-based features extracted from the nominal and flipped DBS-fMRI response maps formed optimal and non-optimal clusters in a neuro-functionally meaningful manner, which indicate robustness of the AE-based feature extraction to subtle differences in the activated regions of DBS-fMRI response maps. The MLP, RF, SVM and LDA methods gave an overall DBS parameter classification accuracy of 96%, 94%, 92% and 93% respectively when trained using the AE-extracted features from the nominal DBS-fMRI maps. The AE-based MLP, RF, SVM and LDA accuracies were higher than the overall accuracy (81%) of our initial parcel-based LDA method. The performance of an AE-MLP model trained using the nominal DBS-fMRI maps did not change significantly when the model was tested on the flipped DBS-fMRI responses. We showed that the MLP method combined with AE-based feature extraction is best suited for fMRI-based DBS parameter optimization and represents another step towards a proposed digital tool for rapid semi-automated biomarker-based DBS optimization.

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Section: Artificial Intelligence Methods For Dbs Parameters Settings ...mentioning

confidence: 99%

Autoencoder-based Feature Extraction and Classification for fMRI-based Deep Brain Stimulation Parameter Optimization for Parkinson’s Disease Treatment: Towards a Rapid Semi-automated Stimulation Optimization

Ajala,

Qiu,

Karigiannis

et al. 2024

Preprint

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“…One potent machine learning technique that is frequently used in the field of medical diagnostics, particularly the diagnosis of Parkinson's disease (PD), is Support Vector Machine (SVM) [9]. Based on a variety of input data sources, including clinical, genetic, and imaging data, SVM's ability to do binary classification has shown to be useful in differentiating between people with Parkinson's disease (PD)…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…deep learning models that are already available which can be utilized in the problem statement [19]. After reviewing different algorithms, here hnique that is frequently used in the field of medical diagnostics, particularly the diagnosis of Parkinson's disease (PD), is Support Vector Machine (SVM) [9]. Based on a bility to do binary classification has shown to be useful in differentiating between people with Parkinson's disease (PD) SVM is a member of a new class of learning systems that draws on recent developments in statistical linear data.…”

Section: Nearest Neighbour (Knn)mentioning

confidence: 99%

Artificial Intelligence for Parkinson’s Disease Prediction

Ravindran,

Kulkarni,

Damle

et al. 2024

Advancements in Communication and Systems

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Parkinson’s disease (PD) is a pervasive neurodegenerative disorder with a diverse clinical presentation and a complex etiological landscape. A comprehensive introduction to the current state of PD research, diagnosis, and management, emphasizing the need for early detection and personalized treatment strategies is presented in this paper. Recognizing the multifactorial nature of PD, the genetic and environmental risk factors, highlighting the role of genetics in monogenic and polygenic PD are explored. Furthermore, the clinical diagnosis of PD, encompassing both motor and non-motor symptoms, and underscore the significance of a prolonged prodromal phase preceding clinical manifestations are discussed. The importance of personalized management, including pharmacological and non-pharmacological interventions, is also emphasized. In the quest for disease modification, the potential of emerging therapies and the ongoing research into the genetic basis of PD is explored. Notably, the promising role of machine learning techniques, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), KNearest Neighbors (KNN), and fuzzy logic, in early PD detection and diagnosis is investigated. By leveraging these advanced computational approaches, there is potential to revolutionize PD diagnosis, providing earlier interventions and tailored treatment strategies. This paper sets the stage for a comprehensive examination of the application of machine learning in PD research and clinical practice. By amalgamating existing knowledge and cutting-edge technologies, the aspiration of this paper is to advance the understanding and management of Parkinson’s disease, ultimately improving the lives of those affected by this challenging condition.

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“…In reality, the two subsets of AI are employed to health data analysis: the first subset is Machine Learning (ML) and the second is Deep Learning (DL) approaches, including radiography images or computed tomography scans, have been shown to be useful on detection of illness and monitoring [12]- [14], [15]- [17]. As a result, various types of human maladies, like as Parkinson's disease [18]- [21], brain tumor segmentation [22], [23], breast cancer [24], diabetes [25], medical image segmentation [26], and heart disease prediction [27]- [30], atherosclerosis diseases [31], could be identified using such techniques. AI advancements have also contributed in the development of a wide range of other scientific fields [32]- [34], [35]- [39].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection on X-Ray Images using a Combining Mechanism of Pre-trained CNNs

Gannour¹,

Hamida²,

Saleh³

et al. 2022

IJACSA

Self Cite

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The COVID-19 infection was sparked by the severe acute respiratory syndrome SARS-CoV-2, as mentioned by the World Health Organization, and originated in Wuhan, Republic of China, eventually extending to every nation worldwide in 2020. This research aims to establish an efficient Medical Diagnosis Support System (MDSS) for recognizing COVID-19 in chest radiography with X-ray data. To build an ever more efficient classifier, this MDSS employs the concatenation mechanism to merge pretrained convolutional neural networks (CNNs) predicated on Transfer Learning (TL) classifiers. In the feature extraction phase, this proposed classifier employs a parallel deep feature extraction approach based on Deep Learning (DL). As a result, this approach increases the accuracy of our proposed model, thus identifying COVID-19 cases with higher accuracy. The suggested concatenation classifier was trained and validated using a Chest Radiography image database with four categories: COVID-19, Normal, Pneumonia, and Tuberculosis during this research. Furthermore, we integrated four separate public X-Ray imaging datasets to construct this dataset. In contrast, our mentioned concatenation classifier achieved 99.66% accuracy and 99.48% sensitivity respectively.

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Parkinson's disease classification using machine learning algorithms: performance analysis and comparison

Cited by 11 publications

References 44 publications

Autoencoder-based Feature Extraction and Classification for fMRI-based Deep Brain Stimulation Parameter Optimization for Parkinson’s Disease Treatment: Towards a Rapid Semi-automated Stimulation Optimization

Autoencoder-based Feature Extraction and Classification for fMRI-based Deep Brain Stimulation Parameter Optimization for Parkinson’s Disease Treatment: Towards a Rapid Semi-automated Stimulation Optimization

Artificial Intelligence for Parkinson’s Disease Prediction

COVID-19 Detection on X-Ray Images using a Combining Mechanism of Pre-trained CNNs

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