scholarly journals Prognostic Factors in Neurorehabilitation of Stroke: A Comparison among Regression, Neural Network, and Cluster Analyses

2021 ◽  
Vol 11 (9) ◽  
pp. 1147
Author(s):  
Marco Iosa ◽  
Giovanni Morone ◽  
Gabriella Antonucci ◽  
Stefano Paolucci
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Prognostic Factors ◽  
Classification Tree ◽  
Large Body ◽  
High Sensitivity ◽  
Test Sample ◽  
Spatial Neglect ◽  
Anterior Circulation ◽  
Cluster Analyses

There is a large body of literature reporting the prognostic factors for a positive outcome of neurorehabilitation performed in the subacute phase of stroke. Despite the recent development of algorithms based on neural networks or cluster analysis for the identification of these prognostic factors, the literature lacks a rigorous comparison among classical regression, neural network, and cluster analysis. Moreover, the three methods have rarely been tested on a sample independent from that in which prognostic factors have been identified. This study aims at providing this comparison on a wide sample of data (1522 patients) and testing the results on an independent sample (1000 patients) using 30 variables. The accuracy was similar among regression, neural network, and cluster analyses on the analyzed sample (76.6%, 74%, and 76.1%, respectively), but on the test sample, the accuracy of neural network decreased (70.1%). The three models agreed in identifying older age, severe impairment, unilateral spatial neglect, and total anterior circulation infarcts as important prognostic factors. The binary regression analysis also provided solid results in the test sample, especially in terms of specificity (81.8%). Cluster analysis also showed a high sensitivity in the test sample (82.6%) and allowed a meaningful easy-to-use classification tree to be obtained.

Public finance flow in contemporary Russia

2020 ◽  
Vol 26 (11) ◽  
pp. 2501-2523
Author(s):  
V.V. Smirnov
Keyword(s):  
Neural Network ◽  
Public Finance ◽  
Systems Approach ◽  
Management Decisions ◽  
Public Authorities ◽  
Cluster Analyses ◽  
The Public ◽  
National Wealth ◽  
Economic Formation

Subject. This article discusses the issues related to public finance. Objectives. The article aims to identify the determinants, indicators, and priorities of the public finance flow in contemporary Russia. Methods. For the study, I used the methods of statistical, neural network, and cluster analyses, and the systems approach. Results. The article identifies and describes the determining indicators of the main aggregates and balances of public finance, sources, and the use of funds. It establishes a link between the main aggregates and balances of public finance, defining the form and content of Russian capitalism. Conclusions. Understanding the issue and problem of public finance flow in contemporary Russia helps identify the reasons for the inability to transit to a capitalist socio-economic formation. The provisions of the study expand the scope of knowledge and develop the competence of public authorities to make management decisions on the distribution and redistribution of the value of a public product and part of the national wealth.

scholarly journals k-Nearest Neighbor Learning with Graph Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 830
Author(s):  
Seokho Kang
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Weighting Function ◽  
High Sensitivity ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Main Challenge ◽  
Benchmark Datasets ◽  
Graph Neural Networks

k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance function, and the weighting function. To improve the robustness to hyperparameters, this study presents a novel kNN learning method based on a graph neural network, named kNNGNN. Given training data, the method learns a task-specific kNN rule in an end-to-end fashion by means of a graph neural network that takes the kNN graph of an instance to predict the label of the instance. The distance and weighting functions are implicitly embedded within the graph neural network. For a query instance, the prediction is obtained by performing a kNN search from the training data to create a kNN graph and passing it through the graph neural network. The effectiveness of the proposed method is demonstrated using various benchmark datasets for classification and regression tasks.

scholarly journals Predicting Increased Blood Pressure Using Machine Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hudson Fernandes Golino ◽  
Liliany Souza de Brito Amaral ◽  
Stenio Fernando Pimentel Duarte ◽  
Cristiano Mauro Assis Gomes ◽  
Telma de Jesus Soares ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Predictive Power ◽  
Classification Tree ◽  
Training Group ◽  
Test Sample ◽  
Hip Circumference ◽  
Training Set ◽  
Learning Technique ◽  
Tree Data

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudoR2(.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudoR2(.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

Collaborative evaluation and management of students’ health-related physical fitness: applications of cluster analysis and the classification tree

2012 ◽  
Vol 24 (2) ◽  
Author(s):  
Jou-An Chen ◽  
Chi-Chuan Shih ◽  
Pay-Fan Lin ◽  
Jin-Jong Chen ◽  
Kuan-Chia Lin
Keyword(s):  
Cluster Analysis ◽  
Physical Fitness ◽  
Classification Tree ◽  
Metabolic Risk ◽  
Tree Model ◽  
School Year ◽  
Classification Tree Model ◽  
Health Related ◽  
Risk Approach ◽  
Collaborative Evaluation

Abstract Health-related physical fitness has decreased with age; this is od immense concern to adolescents. School-based health intervention programs can be classified as either population-wide or high-risk approach. Although the population-wide and risk-based approaches adopt different healthcare angles, they all need to focus resources on risk evaluation. In this paper, we describe an exploratory application of cluster analysis and the tree model to collaborative evaluation of students’ health- related physical fitness from a high school sample in Taiwan (n=742). Cluster analysis show that physical fitness can be divided into relatively good, moderate and poor subgroups. There are significant differences in biochemical measurements among these three groups. For the tree model, we used 2004 school-year students as an experimental group and 2005 school-year students as a validation group. The results indicate that if sit-and-reach is shorter than 33 cm, BMI is >25.46 kg/m2, and 1600 m run/walk is >534 s, the predicted probability for the number of metabolic risk factors ≥2 is 100% and the population is 41, both results are the highest. From the risk-based healthcare viewpoint, the cluster analysis can sort out students’ physical fitness data in a short time and then narrow down the scope to recognize the subgroups. A classification tree model specifically shows the discrimination paths between the measurements of physical fitness for metabolic risk and would be helpful for self-management or proper healthcare education targeting different groups. Applying both methods to specific adolescents’ health issues could provide different angles in planning health promotion projects.

scholarly journals Presymptomatic Prediction of Sepsis in Intensive Care Unit Patients

2008 ◽  
Vol 15 (7) ◽  
pp. 1089-1094 ◽  
Author(s):  
R. A. Lukaszewski ◽  
A. M. Yates ◽  
M. C. Jackson ◽  
K. Swingler ◽  
J. M. Scherer ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care ◽  
Clinical Diagnosis ◽  
Predictive Accuracy ◽  
High Sensitivity ◽  
Icu Patients ◽  
The Neural Network ◽  
Sensitivity And Selectivity ◽  
Ccl2 Mrna ◽  
Factor Α

ABSTRACT Postoperative or posttraumatic sepsis remains one of the leading causes of morbidity and mortality in hospital populations, especially in populations in intensive care units (ICUs). Central to the successful control of sepsis-associated infections is the ability to rapidly diagnose and treat disease. The ability to identify sepsis patients before they show any symptoms would have major benefits for the health care of ICU patients. For this study, 92 ICU patients who had undergone procedures that increased the risk of developing sepsis were recruited upon admission. Blood samples were taken daily until either a clinical diagnosis of sepsis was made or until the patient was discharged from the ICU. In addition to standard clinical and laboratory parameter testing, the levels of expression of interleukin-1β (IL-1β), IL-6, IL-8, and IL-10, tumor necrosis factor-α, FasL, and CCL2 mRNA were also measured by real-time reverse transcriptase PCR. The results of the analysis of the data using a nonlinear technique (neural network analysis) demonstrated discernible differences prior to the onset of overt sepsis. Neural networks using cytokine and chemokine data were able to correctly predict patient outcomes in an average of 83.09% of patient cases between 4 and 1 days before clinical diagnosis with high sensitivity and selectivity (91.43% and 80.20%, respectively). The neural network also had a predictive accuracy of 94.55% when data from 22 healthy volunteers was analyzed in conjunction with the ICU patient data. Our observations from this pilot study indicate that it may be possible to predict the onset of sepsis in a mixed patient population by using a panel of just seven biomarkers.

Humor Style Clusters: Exploring Managerial Humor

2015 ◽  
Vol 55 (4) ◽  
pp. 443-454 ◽  
Author(s):  
Thomas Rhys Evans ◽  
Gail Steptoe-Warren
Keyword(s):  
Job Satisfaction ◽  
Cluster Analysis ◽  
Holistic View ◽  
Cluster Analyses ◽  
Significant Pattern ◽  
Humor Style

The current study is the first to explore the relationships between managerial humor and workplace facets using cluster analysis. Two-hundred and two employed adults rated their managers’ humor and workplace facets online. K-means cluster analyses identified three managerial humor clusters, mostly replicating those found in the existing literature. A significant pattern of differences in stress, communication, creativity, perceptions of leader power, and job satisfaction were found between the clusters. Findings suggest negative humor use is most likely to be damaging to organizations when not used alongside positive humor types, and it is not merely the frequency with which a manager uses an individual humor type, but the holistic view of their humor, which is of importance in gauging valence of organizational facets. Using cluster analysis was beneficial in challenging assumptions from the existing literature, further contextualizing our understanding of humor and reinforcing the importance of humor use in the workplace.

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

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