Early diagnosis of Parkinson’s disease from multiple voice recordings by simultaneous sample and feature selection

Ali, Liaqat; Zhu, Ce; Zhou, Mingyi; Liu, Yipeng

doi:10.1016/j.eswa.2019.06.052

Cited by 125 publications

(64 citation statements)

References 31 publications

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“…e first component of the system is a feature selection module, while the second component is a predictive model. Feature selection methods use data mining concepts to improve the performance of the machine learning models [27,28]. e feature selection module uses a search strategy to find out the optimal subset of features which are applied to the DNN that acts as a predictive model.…”

Section: E Proposed Methodmentioning

confidence: 99%

Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

Javeed

Rizvi

Zhou

et al. 2020

Mobile Information Systems

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Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis. However, low heart disease prediction accuracy is still a problem in these systems. For better heart risk prediction accuracy, we propose a feature selection method that uses a floating window with adaptive size for feature elimination (FWAFE). After the feature elimination, two kinds of classification frameworks are utilized, i.e., artificial neural network (ANN) and deep neural network (DNN). Thus, two types of hybrid diagnostic systems are proposed in this paper, i.e., FWAFE-ANN and FWAFE-DNN. Experiments are performed to assess the effectiveness of the proposed methods on a dataset collected from Cleveland online heart disease database. The strength of the proposed methods is appraised against accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and receiver operating characteristics (ROC) curve. Experimental outcomes confirm that the proposed models outperformed eighteen other proposed methods in the past, which attained accuracies in the range of 50.00–91.83%. Moreover, the performance of the proposed models is impressive as compared with that of the other state-of-the-art machine learning techniques for heart disease diagnosis. Furthermore, the proposed systems can help the physicians to make accurate decisions while diagnosing heart disease.

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Section: E Proposed Methodmentioning

confidence: 99%

Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

Javeed

Rizvi

Zhou

et al. 2020

Mobile Information Systems

View full text Add to dashboard Cite

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“…Feature selection techniques, like chi-squared, LASSO, and genetic algorithm, achieve good classification accuracies of around 90%. Chi-squared statistical model [31] has often used in different studies for feature selection. The difference between Ruzzo-Tompa and chi-squared feature selection is that the aim of Ruzzo-Tompa is to compute the fitness value for each feature in the dataset using the fitness function equation while the aim of chi-squared is to perform a test that measures the dependency between the features and the class.…”

Section: Experimental Results Of Oci-dbn Dnn and Ann On Full Feamentioning

confidence: 99%

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

et al. 2020

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A rapid increase in heart disease has occurred in recent years, which might be the result of unhealthy food, mental stress, genetic issues, and a sedentary lifestyle. There are many advanced automated diagnosis systems for heart disease prediction proposed in recent studies, but most of them focus only on feature preprocessing, some focus on feature selection, and some only on improving the predictive accuracy. In this study, we focus on every aspect that may have an influence on the final performance of the system, i.e., to avoid overfitting and underfitting problems or to solve network configuration issues and optimization problems. We introduce an optimally configured and improved deep belief network named OCI-DBN to solve these problems and improve the performance of the system. We used the Ruzzo-Tompa approach to remove those features that are not contributing enough to improve system performance. To find an optimal network configuration, we proposed a stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured DBN. An analysis of a RBM and DBN trained is performed to give an insight how the system works. Six metrics were used to evaluate the proposed method, including accuracy, sensitivity, specificity, precision, F1 score, and Matthew's correlation coefficient. The experimental results are compared with other state-of-the-art methods, and OCI-DBN shows a better performance. The validation results assure that the proposed method can provide reliable recommendations to heart disease patients by improving the accuracy of heart disease predictions by up to 94.61%.

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“…Three regression methods-Lasso, ridge, and Gaussian process-were tested during the validation experiment performed in this study. Nonlinear regression methods were employed as reported in [55], [56]. As a future endeavor, authors intend to incorporate nonlinear regression methods, including support vector regression and neural networks, among other machine-learning-based alternatives.…”

Section: Discussionmentioning

confidence: 99%

Model of Urine Accumulation in the Bladder and Method for Predicting Unconstrained Urine Volume Based on Absorption Spectrum of Urine

et al. 2020

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As part of their excretion care protocol, many of the senior citizens living in nursing homes are required to wear a diaper for urinary incontinence. However, the diaper must be changed by the caregiver after each incontinence episode, and some of the elderly affected may be unable to inform the caregiver about an episode because of cognitive problems or owing to a sense a shame. Being able to predict the volume of urine in the bladder without having to attach any sensors to the body would make it possible for the caregiver to take the residents to the toilet before they have an incontinence episode. Moreover, the residents would not have to wear a diaper. With this goal in mind, in this study, we developed a macroscopic model for urine accumulation in the bladder as well as a method for predicting the urine volume in the bladder based on the absorption spectrum of urine obtained immediately after urination. The proposed method does not involve attaching a sensor on the skin. We performed a series of experiments to evaluate the proposed method and calculated its error rate based on the actual amount of urine and that predicted using the method. The error rate was then compared with that for an ultrasonic sensor attached to the skin above the bladder. The mean of the error rate of the proposed method was 15.46% whereas that of the ultrasonic sensor was 23.42 %, with the two being significantly different at the 5% level. INDEX TERMS Absorption spectrum, excretion care, Gaussian process regression, hyperspectral imaging, urine volume accumulation.

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Early diagnosis of Parkinson’s disease from multiple voice recordings by simultaneous sample and feature selection

Cited by 125 publications

References 31 publications

Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

Model of Urine Accumulation in the Bladder and Method for Predicting Unconstrained Urine Volume Based on Absorption Spectrum of Urine

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