Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest

Mao, Yuxing; He, Yinghong; Liu, Lumei; Chen, Xueshuo

doi:10.3389/fnins.2020.00798

Cited by 35 publications

(16 citation statements)

References 33 publications

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“…Sajedi et al 5 adopted the Gabor transform method to extract edge features of different directions and intensities of bacterial images for edge detection and pattern recognition. Mao et al 6 proposed a disease classification method based on decision tree and random forest to information such as pupil position and area of eye movement images are extracted as original features. Nevertheless, the traditional recognition methods are insufficient in feature extracting and inefficient, 7 which are not only tedious and time consuming, but also demanding of costly, specialty-oriented knowledge, and skills.…”

Section: Introductionmentioning

confidence: 99%

“…adopted the Gabor transform method to extract edge features of different directions and intensities of bacterial images for edge detection and pattern recognition. Mao et al 6 . proposed a disease classification method based on decision tree and random forest to information such as pupil position and area of eye movement images are extracted as original features.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of honeycomb lung in CT images based on improved MobileNet model

Zhang

Linning

et al. 2021

Medical Physics

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Purpose The research is to improve the efficiency and accuracy of recognition of honeycomb lung in CT images. Methods Deep learning methods are used to achieve automatic recognition of honeycomb lung in CT images, however, are time consuming and less accurate due to the large amount of structural parameters. In this paper, a novel recognition method based on MobileNetV1 network, multiscale feature fusion method (MSFF), and dilated convolution is explored to deal with honeycomb lung in CT image classification. Firstly, the dilated convolution with different dilated rate is used to extract features to obtain receptive fields of different sizes, and then fuse the features of different scales at multiscale feature fusion block is used to solve the problem of feature loss and incomplete feature extraction. After that, by using linear activation functions (Sigmoid) instead of nonlinear activation functions (ReLu) in the improved deep separable convolution blocks to retain the feature information of each channel. Finally, by reducing the number of improved deep separable blocks to reduce the computation and resource consumption of the model. Results The experimental results show that improved MobileNet model has the best performance and the potential for recognition of honeycomb lung image datasets, which includes 6318 images. By comparing with 4 traditional models (SVM, RF, decision tree, and KNN) and 11 deep learning models (LeNet‐5, AlexNet, VGG‐16, GoogleNet, ResNet18, DenseNet121, SENet18, InceptionV3, InceptionV4, Xception, and MobileNetV1), our model achieved the performance with an accuracy of 99.52%, a sensitivity of 99.35%, and a specificity of 99.89%. Conclusion Improved MobileNet model is designed for the automatic recognition and classification of honeycomb lung in CT images. Through experiments comparative analysis of other models of machine learning and deep learning, it is proved that the proposed improved MobileNet method has the best recognition accuracy with fewer the model parameters and less the calculation time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recognition of honeycomb lung in CT images based on improved MobileNet model

Zhang

Linning

et al. 2021

Medical Physics

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“…The coronal features (F1 and F2) and the sagittal features (F3, F4, F5 and F7) proved to be very relevant when classifying the PD stage related to posture in the obtained decision tree model. Besides, we applied an objective function for minimizing calculation errors and searching for the optimal results [ 34 , 35 ]. Both the deviation of the scores and the number of cases with obvious deviation (more than one score of MDS-UPDRS-III 3.13) were considered to ensure that there was less difference between the two scores even in the case where the machine’s and the doctors’ scores were inconsistent.…”

Section: Discussionmentioning

confidence: 99%

Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning

Zhang

Hong

Lin

et al. 2021

J NeuroEngineering Rehabil

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Background Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD). The combination of depth camera and machine learning makes this purpose possible. Methods Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors’ MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale) scores. An objective function was implanted to further improve the human–machine consistency. Results The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine’s and doctors’ score was 0.940 (95%CI, 0.905–0.962), meaning the machine was highly consistent with the doctors’ judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity. Conclusions We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD.

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“…DT expands subtrees and leaves to obtain a node labeled with a predicted outcome category [36]. Application of DT method can be used to prove that the outcome, the inhibition of InhA by ETH, is significantly related to specific residues determined by DT [37].…”

Section: Classification Penalized Logistic Regressionmentioning

confidence: 99%

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

Lee

Maria-Solano

et al. 2022

Biochemical Society Transactions

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There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

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Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest

Cited by 35 publications

References 33 publications

Recognition of honeycomb lung in CT images based on improved MobileNet model

Recognition of honeycomb lung in CT images based on improved MobileNet model

Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

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