scholarly journals Analysis of Influencing Factors on Hospitalization Expenses of Patients with Breast Malignant Tumor Undergoing Surgery: Based on the Neural Network and Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jing Zhang ◽  
Lin Sun
Keyword(s):  
Influencing Factors ◽  
Malignant Tumor ◽  
Descriptive Analysis ◽  
Tertiary Hospital ◽  
Support Vector ◽  
Breast Cancer Patients ◽  
Actual Length ◽  
Treatment Services ◽  
The Neural Network ◽  
Single Factor Analysis

Objective. Analyze the influencing factors of hospitalization expenses of breast cancer patients in a tertiary hospital in Chengdu and provide a basis and suggestion for controlling the unreasonable increase of medical expenses. Methods. The first pages of all inpatient medical records of patients with breast malignant tumor from 2017 to 2020 were extracted, and the descriptive analysis, single-factor analysis, and multifactor analysis were conducted by using the statistical method and data mining method to explore the influencing factors of hospitalization expenses. Results. In 2017–2020, the average hospitalization cost and the average surgical treatment cost increased year by year, and the number of operations, actual hospitalization days, and CCI were the important influencing factors. Conclusion. It is suggested to strengthen the supervision of medical rationality and eliminate the waste of medical resources; and we should improve the efficiency of diagnosis and treatment services, so as to shorten the actual length of hospitalization; at the same time, the combination of DRG grouping and fine management can be used to control the hospitalization expenses.

scholarly journals Descriptive analysis of neurological in-hospital consultations in a tertiary hospital

2017 ◽  
Vol 32 (3) ◽  
pp. 152-157 ◽  
Author(s):  
J.S. Aller-Alvarez ◽  
M. Quintana ◽  
E. Santamarina ◽  
J. Álvarez-Sabín
Keyword(s):  
Descriptive Analysis ◽  
Tertiary Hospital

scholarly journals Factors influencing general practitioners’ perception of and attitude towards dementia diagnostics and care—results of a survey among primary care physicians in Germany

2021 ◽  
Author(s):  
Julian Wangler ◽  
Michael Jansky
Keyword(s):  
Primary Care ◽  
Influencing Factors ◽  
General Practitioners ◽  
Primary Care Physicians ◽  
Descriptive Analysis ◽  
Linear Regression Analysis ◽  
Interdisciplinary Cooperation ◽  
Practice Staff ◽  
People With Dementia ◽  
Dementia Diagnostics

SummaryStudies have shown that primary care is not always effective when it comes to caring for people with dementia. In addition, general practitioners do not always use diagnostic instruments consistently. The aim of the study was to identify relevant factors that influence general practitioners’ attitudes and willingness with respect to consistent diagnosis and care. For this purpose, resources, viewpoints, and behavioral patterns of general practitioners with regard to dementia diagnostics as well as common challenges in everyday practice were recorded. In the course of a survey, a total of 2266 general practitioners in Hesse and Baden-Württemberg were interviewed between January and March 2020. In addition to the descriptive analysis, a t-test was used to determine significant differences between two groups. A univariate linear regression analysis was carried out to identify possible influencing factors. 81% of the respondents do provide dementia diagnostics; 51% are involved in the treatment. Most of them see the diagnostic work-up (77%), communication and compliance problems (73%), as well as the therapeutic support (71%) as common challenges. In addition, there are interface problems regarding the interdisciplinary cooperation. Some of the respondents express doubts about the value of early detection (41%). The general practitioners’ attitude with respect to dementia diagnostics and care is determined by influencing factors that relate to geriatric competencies, expectations of self-efficacy, the integration of practice staff, as well as the knowledge of and cooperation with counseling and care services. It seems advisable to strengthen the geriatric competence of general practitioners. Moreover, it appears essential to educate general practitioners more about support structures in the field of dementia care and to integrate them accordingly. In addition, practice staff should be more systematically involved in the identification and care of dementia patients.

scholarly journals Short-term power load forecasting based on support vector machine and particle swarm optimization

2018 ◽  
Vol 13 ◽  
pp. 174830181879706 ◽  
Author(s):  
Song Qiang ◽  
Yang Pu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Influencing Factors ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Model Parameters ◽  
Short Term ◽  
Swarm Optimization

In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the “abnormal data” in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.

scholarly journals ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy

2019 ◽  
Vol 9 (1) ◽  
pp. 5590-5595
Keyword(s):  
Diabetic Retinopathy ◽  
Research Work ◽  
Texture Features ◽  
Support Vector ◽  
Fundus Images ◽  
Small Vessels ◽  
The Neural Network ◽  
Major Loss ◽  
Occurrence Matrix ◽  
Fluid Leak

The higher levels of blood glucose most often causes a metabolic disorder commonly called as Diabetes, scientifically as Diabetes Mellitus. A consequence of this is a major loss of vision and in long terms may eventually cause complete blindness. It initiates with swelling on blood vessels, formation of microaneurysms at the end of narrow capillaries. Haemorrhages due to rupture of small vessels and fluid leak causes exudates. The specialist examines it to diagnose and gives proper treatment. Fundus images are the fundamental tool for proper diagnosis of patients by medical experts. In this research work the fundus images are taken for processing, the neural network and support vector machine are trained for the proposed model. The features are extracted from the diabetic retinopathy image by using texture based algorithms such as Gabor, Local binary pattern and Gray level co-occurrence matrix for rating the level of diabetic retinopathy. The performance of all methods is calculated based on accuracy, precision, Recall and f-measure.

scholarly journals Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Tuan Vu Dinh ◽  
Hieu Nguyen ◽  
Xuan-Linh Tran ◽  
Nhat-Duc Hoang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Soil Erosion ◽  
Differential Evolution ◽  
Influencing Factors ◽  
Support Vector ◽  
Adaptive Differential Evolution ◽  
Artificial Neural

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

scholarly journals The Trajectory and Influence Factors of Breast Cancer Patients’ Main Chemotherapy-Related Symptoms: A Longitudinal Study

2021 ◽  
Author(s):  
Yishu Qi ◽  
Ning Zhang ◽  
Ye Ma ◽  
Ewen Xu ◽  
Qingmei Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Influencing Factors ◽  
Family Income ◽  
Influence Factors ◽  
Severe Depression ◽  
Stage Ii ◽  
Breast Cancer Patients ◽  
Growth Mixture Model ◽  
Patient Reported

Abstract Introduction: Identifying the pattern of change in symptoms is critical to effective symptom management. This study aimed to determine the trajectory of Main Chemotherapy-related Symptoms (MCRS) in breast cancer patients, explore the influencing factors of potential categories of MCRS trajectory.Methods: Patient-reported Outcomes Measurement System- breast-chemotherapy was used to measure the four highest incidence MCRS (pain, fatigue, anxiety, and depression) weekly in Breast cancer patients. The Growth Mixture Model (GMM) was used to fit the potential categories of the MCRS trajectory. Logistic regression was used to explore the influencing factors of potential categories of MCRS change trajectory.Results: 239 breast cancer patients completed the study. Fatigue and depression showed an overall upward trend during the chemotherapy cycle, while pain and anxiety showed a downward trend. There are two potential categories of anxiety trajectory, three potential categories of fatigue and pain trajectory, and four potential categories of depression trajectory. Compared with the mild-fatigue group, Patients in the moderate and high fatigue groups were more likely to be less educated, have lower household income, and be treated with anthracyclines. Compared with the mild-pain group, patients in the pain-declining and fluctuating-pain groups were young, live-alone, and treated with paclitaxel. Patients in the anxiety-rising group were younger, had premenopausal menstruation with regular monthly menstruation, and had stage II disease. Patients in the depression-rising and severe depression groups were more likely to be solitary and younger.Conclusion: The potential classes of major chemotherapy-related symptom trajectories vary in breast cancer patients. As for fatigue management, great attention should be paid to patients with low education, low family income, and anthracycline chemotherapy. For pain management, close attention should be paid to younger, solitary, and paclitaxel chemotherapy patients; For anxiety management, attention should be paid to younger patients with premenopausal menstruation and regular monthly menstruation patients, and those with stage II disease. In managing depression, attention should be paid to younger and solitary patients.

scholarly journals Design and implementation of an intelligent vision and sorting system

2009 ◽  
Author(s):  
◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Support Vector Machines ◽  
Robot Control ◽  
Support Vector ◽  
Design And Implementation ◽  
The Neural Network ◽  
Vector Machines ◽  
Control Computer ◽  
Sorting System

This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.

scholarly journals Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Nan Liu ◽  
Jiuwen Cao ◽  
Zhi Xiong Koh ◽  
Pin Pin Pek ◽  
Marcus Eng Hock Ong
Keyword(s):  
Emergency Department ◽  
Risk Stratification ◽  
Extreme Learning Machine ◽  
Tertiary Hospital ◽  
Support Vector ◽  
Characteristic Analysis ◽  
Scoring Method ◽  
Stratification Method ◽  
Emergency Department Patients ◽  
Learning Machine

This paper presents a novel risk stratification method using extreme learning machine (ELM). ELM was integrated into a scoring system to identify the risk of cardiac arrest in emergency department (ED) patients. The experiments were conducted on a cohort of 1025 critically ill patients presented to the ED of a tertiary hospital. ELM and voting based ELM (V-ELM) were evaluated. To enhance the prediction performance, we proposed a selective V-ELM (SV-ELM) algorithm. The results showed that ELM based scoring methods outperformed support vector machine (SVM) based scoring method in the receiver operation characteristic analysis.

scholarly journals LEADERSHIP CONCEPT AT SHARIA BANK; A THEORETICAL STUDY

2020 ◽  
Vol 5 (2) ◽  
pp. 21-32
Author(s):  
Havis Aravik ◽  
Dwi Sulastyawati ◽  
Nur Rohim Yunus
Keyword(s):  
Influencing Factors ◽  
Theoretical Study ◽  
Descriptive Analysis ◽  
Clear Picture ◽  
Library Research ◽  
Qualitative Descriptive ◽  
Agent Of Change

The issue of leadership has always been an important and interesting theme to be discussed since it is one of the influencing factors of the success or failure of an organization. Therefore, this article seeks to discuss the sharia bank leadership concept through a theoretical study to provide a clear picture of the leadership concept in sharia banks in theory. This study used library research because all data processed were the library data analyzed with a qualitative-descriptive analysis. The results of this study show that the leadership concept of sharia banks ideally models on the leadership of the Rasulullah (Messenger of Allah) SAW, starting from being smart in communication to being an agent of change, as a coach with the dedication vision and the charismatic spirit.

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