Machine Learning Based Approaches for Prediction of Parkinson's Disease

Tiwari, Arvind Kumar

doi:10.5121/mlaij.2016.3203

Cited by 45 publications

(19 citation statements)

References 13 publications

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“…A bag of machine learning algorithms is tested with the vocal recordings of PD affected individuals to find the best performing model. Random Forest (RF) outperforms with the candidate feature subset identified by minimum Redundancy Maximum Relevance (mRMR) filter approach over other benchmarked models [22].…”

Section: Related Workmentioning

confidence: 99%

Deep Feature Extraction From the Vocal Vectors Using Sparse Autoencoders for Parkinson’s Classification

Xiong

Yao-hua

2020

IEEE Access

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Parkinson's disease (PD) is a chronic, neurodegenerative disorder that mainly affects the motor system of the body. The progression rate is very slow but, as the condition worsens it stimulates the non-motor characteristics, especially mood disorders, hallucinations, delusions, and other observable cognitive changes. Genetic and environmental factors greatly influence the risk of Parkinson's development. The early diagnosis of PD improves the condition through proper treatment. During the initial stage of PD, vocal impairments become more common among the affected individuals. Advanced studies on vocal disorders give more assistance for precise PD detection. In this paper, the vocal features of PD affected individuals are analyzed with sophisticated computational models. Initially, the samples are pre-processed, as it contains more missing values. Then the predictor candidate subset is identified from the processed vocal features using the Adaptive Grey Wolf Optimization Algorithm, a meta-heuristic global search optimization technique. Furthermore, the latent representation of the candidate features is extracted through sparse autoencoders for effective discrimination between the PD affected and control cases. For classification, six supervised machine learning algorithms were employed. The proposed model is trained with the data, divided through 10-fold cross-validation scheme and the performance is evaluated based on validation metrics. The PD dataset is accessed from University of California (UCI), Irvine Machine Learning repository to conduct the experimental analysis. The result shows that the proposed algorithm outperformed the benchmarked models, exhibits its efficacy in distinguishing the affected and healthy samples of PD. The outcome of this study emphasizes the significance of intelligent learning models in complex disease diagnosis.

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Section: Related Workmentioning

confidence: 99%

Deep Feature Extraction From the Vocal Vectors Using Sparse Autoencoders for Parkinson’s Classification

Xiong

Yao-hua

2020

IEEE Access

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“…Support Vector Machine (SVM) with non-linear kernel, naïve Bayes classifier, Random Forest, Bagging, and AdaBoost have been shown to be powerful supervised learning techniques for sensorbased gait classification [28], [29], [54]. We used Naïve Bayes, SVM, Random Forest, Bagging, and AdaBoost classifiers using a repeated 5-fold cross validation method in our analysis and compared the accuracy of each model across different feature sets and across standardized and non-standardized vectors of feature.…”

Section: Classification Modelsmentioning

confidence: 99%

Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis

Rastegari¹,

Azizian²,

Ali³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Parkinson's Disease is a worldwide health problem, causing movement disorder and gait deficiencies. Automatic noninvasive techniques for Parkinson's disease diagnosis is appreciated by patients, clinicians and neuroscientists. Gait offers many advantages compared to other biometrics specifically when data is collected using wearable devices; data collection can be performed through inexpensive technologies, remotely, and continuously. In this study, a new set of gait features associated with Parkinson's Disease are introduced and extracted from accelerometer data. Then, we used a feature selection technique called maximum information gain minimum correlation (MIGMC). Using MIGMC, features are first reduced based on Information Gain method and then through Pearson correlation analysis and Tukey post-hoc multiple comparison test. The ability of several machine learning methods, including Support Vector Machine, Random Forest, AdaBoost, Bagging, and Naïve Bayes are investigated across different feature sets. Similarity Network analysis is also performed to validate our optimal feature set obtained using MIGMC technique. The effect of feature standardization is also investigated. Results indicates that standardization could improve all classifiers' performance. In addition, the feature set obtained using MIGMC provided the highest classification performance. It is shown that our results from Similarity Network analysis are consistent with our results from the classification task, emphasizing on the importance of choosing an optimal set of gait features to help objective assessment and automatic diagnosis of Parkinson's disease. Results illustrate that ensemble methods and specifically boosting classifiers had better performances than other classifiers. In summary, our preliminary results support the potential benefit of accelerometers as an objective tool for diagnostic purposes in PD.

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“…Multilayer perceptron (MLP), support vector machine (SVM), RF, and logistic regression (LR) methods were used for building classification models. These methods were adopted because of their successful applications to medical datasets for disease classification [13][14][15][16][17][18].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study

Mohammed¹,

Podila²,

Davis³

et al. 2020

J Med Internet Res

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Background Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which may progress to organ failure during disease exacerbations. Early detection of acute physiological deterioration leading to organ failure is not always attainable. Machine learning techniques that allow for prediction of organ failure may enable early identification and treatment and potentially reduce mortality. Objective The aim of this study was to test the hypothesis that machine learning physiomarkers can predict the development of organ dysfunction in a sample of adult patients with SCD admitted to intensive care units (ICUs). Methods We applied diverse machine learning methods, statistical methods, and data visualization techniques to develop classification models to distinguish SCD from controls. Results We studied 63 sequential SCD patients admitted to ICUs with 163 patient encounters (mean age 30.7 years, SD 9.8 years). A subset of these patient encounters, 22.7% (37/163), met the sequential organ failure assessment criteria. The other 126 SCD patient encounters served as controls. A set of signal processing features (such as fast Fourier transform, energy, and continuous wavelet transform) derived from heart rate, blood pressure, and respiratory rate was identified to distinguish patients with SCD who developed acute physiological deterioration leading to organ failure from patients with SCD who did not meet the criteria. A multilayer perceptron model accurately predicted organ failure up to 6 hours before onset, with an average sensitivity and specificity of 96% and 98%, respectively. Conclusions This retrospective study demonstrated the viability of using machine learning to predict acute organ failure among hospitalized adults with SCD. The discovery of salient physiomarkers through machine learning techniques has the potential to further accelerate the development and implementation of innovative care delivery protocols and strategies for medically vulnerable patients.

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Machine Learning Based Approaches for Prediction of Parkinson's Disease

Abstract: The prediction of Parkinson's

Cited by 45 publications

References 13 publications

Deep Feature Extraction From the Vocal Vectors Using Sparse Autoencoders for Parkinson’s Classification

Deep Feature Extraction From the Vocal Vectors Using Sparse Autoencoders for Parkinson’s Classification

Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study

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