The aim of this study is to analyze the risk factors associated with the development of adenomatous and malignant polyps in the gallbladder. Adenomatous polyps of the gallbladder are considered precancerous and have a...
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The aim of this study is to analyze the risk factors associated with the development of adenomatous and malignant polyps in the gallbladder. Adenomatous polyps of the gallbladder are considered precancerous and have a high likelihood of progressing into malignancy. Preoperatively, distinguishing between benign gallbladder polyps, adenomatous polyps, and malignant polyps is challenging. Therefore, the objective is to develop a neural network model that utilizes these risk factors to accurately predict the nature of polyps. This predictivemodel can be employed to differentiate the nature of polyps before surgery, enhancing diagnostic accuracy. A retrospective study was done on patients who had cholecystectomy surgeries at the Department of Hepatobiliary Surgery of the Second People's Hospital of Shenzhen between January 2017 and December 2022. The patients' clinical characteristics, lab results, and ultrasonographic indices were examined. Using risk variables for the growth of adenomatous and malignant polyps in the gallbladder, a neural network model for predicting the kind of polyps will be created. A normalized confusion matrix, PR, and ROC curve were used to evaluate the performance of the model. In this comprehensive study, we meticulously analyzed a total of 287 cases of benign gallbladder polyps, 15 cases of adenomatous polyps, and 27 cases of malignant polyps. The data analysis revealed several significant findings. Specifically, hepatitis B core antibody (95% CI -0.237 to 0.061, p < 0.001), number of polyps (95% CI -0.214 to -0.052, p = 0.001), polyp size (95% CI 0.038 to 0.051, p < 0.001), wall thickness (95% CI 0.042 to 0.081, p < 0.001), and gallbladder size (95% CI 0.185 to 0.367, p < 0.001) emerged as independent predictors for gallbladder adenomatous polyps and malignant polyps. Based on these significant findings, we developed a predictive classification model for gallbladder polyps, represented as follows, predictive classification model for GBPs = -
A novel approach of data augmentation based on irregular superpixel decomposition is proposed. This approach called SuperpixelGridMasks permits to extend original image datasets that are required by training stages of...
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A novel approach of data augmentation based on irregular superpixel decomposition is proposed. This approach called SuperpixelGridMasks permits to extend original image datasets that are required by training stages of machine learning-related analysis architectures towards increasing their performances. Three variants named SuperpixelGridCut, SuperpixelGridMean, and SuperpixelGridMix are presented. These grid-based methods produce a new style of image transformations using the dropping and fusing of information. Extensive experiments using various image classificationmodels as well as precision health and surrounding real-world datasets show that baseline performances can be significantly outperformed using our methods. The comparative study also shows that our methods can overpass the performances of other data augmentations. SuperpixelGridCut, SuperpixelGridMean, and SuperpixelGridMix codes are publicly available at .
As both localization and globalization continue to demand for knowledgeable and skilled individuals, it is imperative that higher education institutions (HEIs) produce quality graduates that can cope-up with it. Since...
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ISBN:
(纸本)9781450354141
As both localization and globalization continue to demand for knowledgeable and skilled individuals, it is imperative that higher education institutions (HEIs) produce quality graduates that can cope-up with it. Since then, this has been the goal of many of these institutions in the Philippines. However, the efforts of these institutions to deliver quality education aimed to equip the students often fall short when students are found to be under-performing academically because of over-exposure and addiction to social media. The challenge now with educators is to assess the performance of these students at an early stage and do the necessary interventions. With this, the researcher saw an opportunity to create a predictive classification model that can be used in predicting the academic performance of higher education students. Through Support Vector Machine (SVM), the academic profile of past students are categorized into performing and under-performing and this formed part of the predictive classification model. The accuracy of the model have been measured through cross-validation. The test results revealed that the model were able to appropriately predict if a student needs intervention or not with respect to academic performance.
As both localization and globalization continue to demand for knowledgeable and skilled individuals, it is imperative that higher education institutions（HEIs） produce quality graduates that can cope-up with it. Sinc...
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As both localization and globalization continue to demand for knowledgeable and skilled individuals, it is imperative that higher education institutions（HEIs） produce quality graduates that can cope-up with it. Since then, this has been the goal of many of these institutions in the Philippines. However, the efforts of these institutions to deliver quality education aimed to equip the students often fall short when students are found to be underperforming academically because of over-exposure and addiction to social media. The challenge now with educators is to assess the performance of these students at an early stage and do the necessary interventions. With this, the researcher saw an opportunity to create a predictive classification model that can be used in predicting the academic performance of higher education students. Through Support Vector Machine（SVM）, the academic profile of past students are categorized into performing and underperforming and this formed part of the predictive classification model. The accuracy of the model have been measured through cross-validation. The test results revealed that the model were able to appropriately predict if a student needs intervention or not with respect to academic performance.
Business health prediction is critical and challenging in today's volatile environment, thus demand going beyond classical business failure studies underpinned by rigidities, like paired sampling, a-priori predict...
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Business health prediction is critical and challenging in today's volatile environment, thus demand going beyond classical business failure studies underpinned by rigidities, like paired sampling, a-priori predictors, rigid binary categorization, amongst others. In response, our paper proposes an investor-facing dynamic model for characterizing business health by using a mixed set of techniques, combining both classical and "expert system" methods. Data for constructing the model was obtained from 198 multinational manufacturing and service firms spread over 26 industrial sectors, through Wharton database. The novel 4-stage methodology developed combines a powerful stagewise regression for dynamic predictor selection, a linear regression for modelling expert ratings of firms' stock value, an SVM model developed from unmatched sample of firms, and finally an SVM-probability model for continuous classification of business health. This hybrid methodology reports comparably higher classification and prediction accuracies (over 0.96 and similar to 90%, respectively) and predictor extraction rate (similar to 96%). It can also objectively identify and constitute new unsought variables to explain and predict behaviour of business subjects. Among other results, such a volatile model build upon a stable methodology can influence business practitioners in a number of ways to monitor and improve financial health. Future research can concentrate on adding a time-variable to the financial model along with more sector-specificity. (C) 2015 Elsevier Ltd. All rights reserved.
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