Association analysis of Parkinson disease with vocal change characteristics using multi-objective metaheuristic optimization

Altay, Elif Varol; Alataş, Bilal

doi:10.1016/j.mehy.2020.109722

Cited by 15 publications

(9 citation statements)

References 24 publications

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“…Although various meta-heuristic algorithms such as GWO, GBO, SMA, and HHO etc. successfully integrated into many applications [42]- [44], hyper-parameter optimization in expensive-to-evaluate objective function e.g., 10-fold cross-validation loss, used in this study, makes it more complicated [45]. Besides, meta-heuristic algorithms require a set of input parameters that need to be found out to obtain an improved performance as the performance of the meta-heuristic algorithms are very sensitive to the VOLUME 9, 2021 input parameters.…”

Section: Requirement Of Optimizationmentioning

confidence: 99%

A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data

et al. 2021

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The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.

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Section: Requirement Of Optimizationmentioning

confidence: 99%

A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data

et al. 2021

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“…Read speech includes standardised (36,42,47,50) and custom (51)(52)(53)(54)(55)(56)(57)(58)(59)(60) sentences or text passages, such as 'the north wind and the sun' (46,61), 'the rainbow passage' (62,63), and other passages (64,65) as well as disease specific tasks, such as constructed sentences with emotionally evoking words for depression quantification (31,66). Especially in PD, utilising sustained phonation of the vowel /a/ appear to be popular [e.g., (60,(67)(68)(69)(70)(71)]. The most specific speech task used was diadochokinesis (DD), which was only used in datasets concerned with PD [e.g., (67)].…”

Section: Speech Tasksmentioning

confidence: 99%

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Hecker

Steckhan

Eyben

et al. 2022

Front. Digit. Health

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Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.

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“…DISCUSSION In this study, an SVM-based learning model was proposed for PD classification using a voice database, as previous results showed that PD patients often present voice disorders [18]. Voice detection was conducted due to the potential inaccuracies of some clinical methods during diagnosis, making early detection a complex and time-consuming process [15]. Before starting training and classification, preprocessing was performed to ensure optimal data performance.…”

Section: = (4)mentioning

confidence: 99%

“…Neurologists have previously attempted diagnosis using methods including blood tests, neuroimaging, physical examinations, and medical history of patients. However, the initial diagnosis proved inaccurate because of symptoms resemblance to those of other neurological disorders such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA) [15]. The clinical evaluation mainly relies on subjective assessments of motor symptoms and responses to dopaminergic medications, which can be confused with other movement disorders.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Jumanto,

Rofik,

Sugiharti

et al. 2024

J. Inf. Syst. Eng. Bus. Intell.

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Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics. Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability. Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech

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Association analysis of Parkinson disease with vocal change characteristics using multi-objective metaheuristic optimization

Cited by 15 publications

References 24 publications

A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data

A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

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