SRC: Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

Swain, Basanta Kumar; Khan, Mohammad Zubair; Chowdhary, Chiranji Lal; Alsaeedi, Abdullah

doi:10.32604/csse.2023.036192

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…With the rise of machine learning and deep learning, new techniques of medical diagnostic methods have been opened up to complement the expertise of physicians. Many studies such as Parkinson's disease [9,10], covid-19 [11,12] have already been made on a range of medical diagnostic topics with different levels of accuracy [3]. Ihe internet of things also playing a key role in health care systems [13].…”

Section: Introductionmentioning

confidence: 99%

An Insight Into Viable Machine Learning Models for Early Diagnosis of Cardiovascular Disease

Chalapathi,

Vali,

Kumar

et al. 2024

SCPE

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Cardiovascular diseases (CVD) are a prominent source of death across the globe, and these deaths are taking place in low-to middle-income nations. Due to this, CVD prevention is a pressing issue that has already been the subject of extensive research. Innovative methodologies in machine learning (ML) can have a greater impact on the diagnosis of CVD, yet the research on CVD is more challenging and attracting more research indeed. In this paper, we investigate the differences between four distinct machine learning models, support vector machine (SVM), logistic regression, decision trees (DT), and artificial neural networks (ANN) in their classification accuracy and possible practicality in CVD classification. techniques such as ensemble learning and other model-specific optimizations are not part of this study, but more basic implementations of the various models were used. To implement abovementioned ML models, a subset of 14 features from the original heart disease dataset is considered and deemed relevant for classification where no individual feature data are missing. From the results, it is observed that there is no clear winner in the comparison of models. There is no significant difference in the average accuracy of models. The highest average hit rate is observed in SVM and ANN, however it is slightly lower in ANN. Even though the DT had lower accuracy, the fully trained model can be easily visualized and interpreted by humans. Hence, the DT is possibly the most practical model to use as a complement to doctors in their current methods of diagnosis.

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Section: Introductionmentioning

confidence: 99%

An Insight Into Viable Machine Learning Models for Early Diagnosis of Cardiovascular Disease

Chalapathi,

Vali,

Kumar

et al. 2024

SCPE

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“…Learning based approach have great contribution in the prediction of autism disease [5], [29]. Parisot et al [25] built a population graph using a graph-based technique and trained a graph convolution network (GCN) to do so.…”

mentioning

confidence: 99%

Efficient Net-based Transfer Learning Technique for Facial Autism Detection

Saeed Mian

2023

SCPE

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Autism Spectrum Disorder is a neurological disorder in which an individual faces life-long effects in communication and interaction with others. Nowadays, the Autism Spectrum disorder ratio is increasing drastically more than ever before. Autism can be identified at all developmental levels as a ”behavioural condition,” and its symptoms often arise between the ages of two and four. The ASD issue starts during puberty and persists through adolescence and adulthood. Children with ASD use both nonverbal and verbal behaviour to communicate, and they struggle with joint attention and social reciprocity. Children with autism are frequently socially isolated as a result of these problems. Through very expensive and time-consuming screening exams, autism spectrum features can be identified. As one of the possible mirrors of the brain, children’s faces can be utilised as a biomarker and as a quick and convenient technique for the early identification of ASD. An effective, genuine, and automatic method of face-based spectrum disorder identification is required. In this study we compare the transfer learning approach used for autism identification with the convolutional neural network (CNN)-based efficient-net strategy to identify autistic children using facial images. We used an open-source Kaggle dataset and evaluated the model performance in terms of accuracy, confusion matrix, precision, recall, and F1 measure. Efficient shows an accuracy of 97% on the benchmark dataset and beats the baseline technique of transfer learning-based approaches. This study can be used to help medical professionals validate their initial screening procedures for finding youngsters with ASD disease.

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SUPER-COUGH: A Super Learner-based ensemble machine learning method for detecting disease on cough acoustic signals

Topuz,

Kaya

2024

Biomedical Signal Processing and Control

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SRC: Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

Cited by 3 publications

References 36 publications

An Insight Into Viable Machine Learning Models for Early Diagnosis of Cardiovascular Disease

An Insight Into Viable Machine Learning Models for Early Diagnosis of Cardiovascular Disease

Efficient Net-based Transfer Learning Technique for Facial Autism Detection

SUPER-COUGH: A Super Learner-based ensemble machine learning method for detecting disease on cough acoustic signals

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