Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning

Nilashi, Mehrbakhsh; Abumalloh, Rabab Ali; Minaei‐Bidgoli, Behrouz; Samad, Sarminah; Ismail, Muhammed Yousoof; Alhargan, Ashwaq; Zogaan, Waleed Abdu

doi:10.1155/2022/2793361

Cited by 50 publications

(9 citation statements)

References 102 publications

(130 reference statements)

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“…The objective of SOM is to transfer all input data objects with n dimensions to the output in a manner in which the objects are related to each other [55,56]. The SOM is able to perform unsupervised training for the datasets, in which the target is clustering the data [14,15,[57][58][59][60][61]. The Euclidean distance [62,63] from the input vector to all nodes is computed when the input vector is given to the network.…”

Section: Sommentioning

confidence: 99%

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A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches

et al. 2023

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Diabetes in humans is a rapidly expanding chronic disease and a major crisis in modern societies. The classification of diabetics is a challenging and important procedure that allows the interpretation of diabetic data and diagnosis. Missing values in datasets can impact the prediction accuracy of the methods for the diagnosis. Due to this, a variety of machine learning techniques has been studied in the past. This research has developed a new method using machine learning techniques for diabetes risk prediction. The method was developed through the use of clustering and prediction learning techniques. The method uses Singular Value Decomposition for missing value predictions, a Self-Organizing Map for clustering the data, STEPDISC for feature selection, and an ensemble of Deep Belief Network classifiers for diabetes mellitus prediction. The performance of the proposed method is compared with the previous prediction methods developed by machine learning techniques. The results reveal that the deployed method can accurately predict diabetes mellitus for a set of real-world datasets.

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Section: Sommentioning

confidence: 99%

“…The use of machine learning in disease diagnosis has been effective [9][10][11][12][13][14][15][16][17][18][19][20]. Many algorithms have been developed using supervised and unsupervised learning techniques and the deployment of real-world datasets for disease prediction [21,22].…”

Section: Introductionmentioning

confidence: 99%

A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches

et al. 2023

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“…Currently, there is no effective cure for PD, and available treatments mainly focus on symptom management ( 5 ). Consequently, early diagnosis and detection of PD are of paramount importance ( 6 ). An efficient early diagnosis enables the timely detection of health issues and the implementation of intervention to minimize the severe health risks associated with the disease ( 1 , 2 ).…”

Section: Introductionmentioning

confidence: 99%

White matter biomarker for predicting de novo Parkinson’s disease using tract-based spatial statistics: a machine learning-based model

Zhang,

Wang,

Shi

et al. 2024

Quant Imaging Med Surg

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Background Parkinson’s disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. Methods This study selected 227 cases from the Parkinson’s Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case’s fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model. Results In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. Conclusions The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.

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“…The UPDRS is regarded as the most popular clinical rating system and a reliable test for evaluating Parkinson's patients. [2] Our body is primarily controlled by the brain. As a result, any harm to this delicate area of a human body will have a negative impact but in the other organs.…”

Section: Introductionmentioning

confidence: 99%

Parkinson Disease Prediction Using Deep Learning Algorithm

Suchitra

2023

Recent Trends in Data Science and Its Applications

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Parkinson's is a neurological disorder that worsens over time. People have trouble communicating, writing, walking, or doing other basic activities once dopamine-producing neurons in specific regions of the brain are damaged or die. Over time, these symptoms worsen the seriousness of the patients' situation. Using Parkinson's Visual Large Dataset from UCI patient populations, which would be dependent on healthy as well as unhealthy of spiral and wave data, we present a technique in this study for determining the prevalence of Parkinson's disease. We have designed a neural network to detect the disorder and predict the severity of the condition. Here, we identify Parkinson's disease using two deep learning models: the convolution neural network and the Alex net. Additionally, this project seeks to investigate how to recognize the Parkinson patient using image data such as healthy and unhealthy spiral and wave data since various databases may record various aspects of this disease. Here, we employ a five-step process that includes data gathering, preprocessing, model application, classification, and estimation based on user-selected input data. Results of the experiments demonstrated the system's improved efficiency. Finally, we compare the classification report and model accuracy score to demonstrate which algorithm is optimal for the system's prediction.

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Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning

Cited by 50 publications

References 102 publications

A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches

A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches

White matter biomarker for predicting de novo Parkinson’s disease using tract-based spatial statistics: a machine learning-based model

Parkinson Disease Prediction Using Deep Learning Algorithm

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