An Optimized Approach to Huntington’s Disease Detecting via Audio Signals Processing with Dimensionality Reduction

Guimarães, Matheus T.; Medeiros, Aldísio G.; Almeida, Jefferson S.; Martin, Marcos Falcao y; Damaševičius, Robertas; Maskeliūnas, Rytis; Mattos, César Lincoln C.; Filho, Pedro Pedrosa Rebouças

doi:10.1109/ijcnn48605.2020.9206773

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…In short, in the application scenario of nursing homes, the existing research cannot meet the needs of using audio for disease risk prediction, and the Risevi model we propose meets the current application needs of nursing homes. [17] Dementia KNN, SVM 97.2% S. Aich [19] Parkinson's disease linear classification 97.57% D. Pettas [20] Lung Disease LSTM 92.76% K. Sriskandaraja [26] Dementia KNN 91% M. T. Guimarães [27] Huntington's disease KNN 99% V. Ramesh [29] Cough GAN 76% Y. F. Khan [37] Alzheimer's Disease CNN, LSTM 85.05% S. Kamepalli [40] Cardiac LSTM 85%…”

Section: Related Workmentioning

confidence: 99%

“…Disease Basic Algorithm Accuracy M. V. A. Rao [10] Asthma SVR 77.77% Y. You [17] Dementia KNN, SVM 97.2% S. Aich [19] Parkinson's disease linear classification 97.57% D. Pettas [20] Lung Disease LSTM 92.76% K. Sriskandaraja [26] Dementia KNN 91% M. T. Guimarães [27] Huntington's disease KNN 99% V. Ramesh [29] Cough GAN 76% Y. F. Khan [37] Alzheimer's Disease CNN, LSTM 85.05% S. Kamepalli [40] Cardiac LSTM 85%…”

Section: Researchermentioning

confidence: 99%

“…The model achieved 91% accuracy in experiments. M. T. Guimarães et al used digitized speech signals of Huntington's disease patients and healthy volunteers to read Lithuanian poetry[27]. They proposed a prediction method for Huntington's disease based on the KNN classifier.…”

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confidence: 99%

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Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

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The intensification of population aging has brought pressure on public medical care. In order to reduce this pressure, we combined the image classification method with computer vision and used audio data that is easy to collect in nursing homes. Based on MelGAN, transfer learning, and Vision Transformer, we propose an application called Risevi (A Disease Risk Prediction Model Based on Vision Transformer), a disease risk prediction model for nursing homes. We first design a sample generation method based on MelGAN, then refer to the Mel frequency cepstral coefficient and the Wav2vec2 model to design the sample feature extraction method, perform floating-point operations on the tensor of the extracted features, and then convert it into a waveform. We then design a sample feature classification method based on transfer learning and Vision Transformer. Finally, we obtain the Risevi model. In this paper, we use public datasets and subject data as sample data. The experimental results show that the Risevi model has achieved an accuracy rate of 98.5%, a precision rate of 96.38%, a recall rate of 98.17%, and an F1 score of 97.15%. The experimental results show that the Risevi model can provide practical support for reducing public medical pressure.

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Section: Related Workmentioning

confidence: 99%

Section: Researchermentioning

confidence: 99%

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Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

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“…Machine learning (ML) algorithms are used to solve problems by analyzing and interpreting large volumes of data to solve problems in the medical sector [16][17][18][19][20]. Several researchers have used machine learning algorithms to solve medical difficulties in this area.…”

Section: Introductionmentioning

confidence: 99%

Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques

et al. 2021

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The application of machine learning techniques to the epidemiology of COVID-19 is a necessary measure that can be exploited to curtail the further spread of this endemic. Conventional techniques used to determine the epidemiology of COVID-19 are slow and costly, and data are scarce. We investigate the effects of noise filters on the performance of machine learning algorithms on the COVID-19 epidemiology dataset. Noise filter algorithms are used to remove noise from the datasets utilized in this study. We applied nine machine learning techniques to classify the epidemiology of COVID-19, which are bagging, boosting, support vector machine, bidirectional long short-term memory, decision tree, naïve Bayes, k-nearest neighbor, random forest, and multinomial logistic regression. Data from patients who contracted coronavirus disease were collected from the Kaggle database between 23 January 2020 and 24 June 2020. Noisy and filtered data were used in our experiments. As a result of denoising, machine learning models have produced high results for the prediction of COVID-19 cases in South Korea. For isolated cases after performing noise filtering operations, machine learning techniques achieved an accuracy between 98–100%. The results indicate that filtering noise from the dataset can improve the accuracy of COVID-19 case prediction algorithms.

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“…They achieved an accuracy of 96.3% with WST-SVM and 94.50% with BiLSTM. Matheus T et al [26] developed an algorithm to detect Huntington's disease from voice recordings of patients reading Lithuanian poems. They estimated twelve new signal feature extractors by open-SMILE (open source media interpretation by large feature-space extraction) and integrated with KNN (K-nearest neighbours), SVM, MLP (multilayer perceptron), LDA, and QDA (quadratic discriminant analysis) models.…”

Section: Introductionmentioning

confidence: 99%

Weighted Hybrid Feature Reduction Embedded with Ensemble Learning for Speech Data of Parkinson’s Disease

et al. 2021

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Parkinson’s disease (PD) is a progressive and long-term neurodegenerative disorder of the central nervous system. It has been studied that 90% of the PD subjects have voice impairments which are some of the vital characteristics of PD patients and have been widely used for diagnostic purposes. However, the curse of dimensionality, high aliasing, redundancy, and small sample size in PD speech data bring great challenges to classify PD objects. Feature reduction can efficiently solve these issues. However, existing feature reduction algorithms ignore high aliasing, noise, and the stability of algorithms, and thus fail to give substantial classification accuracy. To mitigate these problems, this study proposes a weighted hybrid feature reduction embedded with ensemble learning technique which comprises (1) hybrid feature reduction technique that increases inter-class variance, reduces intra-class variance, preserves the neighborhood structure of data, and remove co-related features that causes high aliasing and noise in classification. (2) Weighted-boosting method to train the model precisely. (3) Furthermore, the stability of the algorithm is enhanced by introducing a bagging strategy. The experiments were performed on three different datasets including two widely used datasets and a dataset provided by Southwest Hospital (Army Military Medical University) Chongqing, China. The experimental results indicated that compared with existing feature reduction methods, the proposed algorithm always shows the highest accuracy, precision, recall, and G-mean for speech data of PD. Moreover, the proposed algorithm not only shows excellent performance for classification but also deals with imbalanced data precisely and achieved the highest AUC in most of the cases. In addition, compared with state-of-the-art algorithms, the proposed method shows improvement up to 4.53%. In the future, this algorithm can be used for early and differential diagnoses, which are rated as challenging tasks.

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An Optimized Approach to Huntington’s Disease Detecting via Audio Signals Processing with Dimensionality Reduction

Cited by 7 publications

References 23 publications

Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques

Weighted Hybrid Feature Reduction Embedded with Ensemble Learning for Speech Data of Parkinson’s Disease

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