Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

Raheja, Supriya; Kasturia, Shreya; Cheng, Xiaochun; Kumar, Manoj

doi:10.1007/s00521-021-06376-x

Cited by 26 publications

(15 citation statements)

References 61 publications

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“…Machine learning (ML) algorithms are usually used in data classification problems (Aggarwal et al 2021;Raheja et al 2021;Thapliyal et al 2021;Chakradar et al 2021). The most important step of ML is to successfully extract the essential features that guarantee robust classification.…”

Section: Introductionmentioning

confidence: 99%

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Yousif

Balaha

Haikal

et al. 2022

J Ambient Intell Human Comput

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Parkinson’s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features; and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches.

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Section: Introductionmentioning

confidence: 99%

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Yousif

Balaha

Haikal

et al. 2022

J Ambient Intell Human Comput

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“…Specifically, the proposed HQCS has low time complexity and higher scalability, showing the application prospects in medical diagnosis. Next, ways to optimize the HQCS strategy combined with machine learning [29] and apply the registration results to disease detection will be considered in our future work.…”

Section: Discussionmentioning

confidence: 99%

A Fast Image Guide Registration Supported by Single Direction Projected CBCT

Gong

Xie

et al. 2022

Electronics

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Image registration is an important research topic in medical image-guided therapy, which is dedicated to registering the high-dose imaging sequences with low-dose/faster means. Registering computer tomography (CT) scanning sequences with cone beam computer tomography (CBCT) scanning sequences is a typical application and has been widely used in CBCT-guided radiotherapy. The main problem is the difference in image clarity of these two image sequences. To solve this problem, for the single projection image sequence matching tasks encountered in medical practice, a novel local quality based curved section encoding strategy is proposed in this paper, which is called the high-quality curved section (HQCS). As an optimized cross-section regularly encoded along the sequence of image, this curved section could be used in order to solve the matching problem. Referencing the independent ground truth provided by medical image physicians, with an experiment combined with the four most widely used indicators used on image registration, matching performance of HQCS on CT/CBCT datasets was tested with varying clarity. Experimental results show that the proposed HQCS can register the CT/CBCT effectively and outperforms the commonly used methods. Specifically, the proposed HQCS has low time complexity and higher scalability, which indicates that the application enhanced the task of diagnosis.

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“…The number of network parameters was effectively reduced and the computational complexity was greatly reduced. It had been used as a neural network model to predict the risk of various diseases in recent years [ 53 , 54 ], but the prediction effect of CNN on different diseases was unstable. For example, Dai G et al used CNN model to explore the effect of hypertension on the retinal microvascular system, and the results were not satisfactory, with a sensitivity of 60.94%, a specificity of 51.54% and an AUC of 0.6506, which may be due to the fact that the model construction needed to be further improved [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers

Zheng¹,

Chen²,

Yang³

et al. 2022

IJERPH

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The dark and humid environment of underground coal mines had a detrimental effect on workers’ skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specificity was 80.986, 89.448, 91.866, the F1 scores was 0.618, 0.919, 0.740, the Brier scores was 0.153, 0.040, 0.156, and the Calibration-in-the-large was 0.104, 0.020, 0.076, respectively, XG Boost outperformed the other two models. Similar results were obtained for the test set and validation set. A two-by-two comparison of the area under the ROC curve (AUC) of the three models showed that the XG Boost model had the best prediction performance. The XG Boost model had a high application value and outperformed the CNN and Logistic regression models in prediction.

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Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

Cited by 26 publications

References 61 publications

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

A Fast Image Guide Registration Supported by Single Direction Projected CBCT

A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers

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