scholarly journals Performance Evaluation of Diagnosis Chronic Kidney Disease using Support Vector Machine and Logistic Regression Model

2019 ◽  
Vol 14 (15) ◽  
pp. 5167-5175
Author(s):  
Rizgar Maghdid Ahmed ◽  
Omar Qusay Alshebly
Keyword(s):  
Chronic Kidney Disease ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Performance Evaluation ◽  
Kidney Disease ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Support Vector

scholarly journals Identification of Accounting Fraud Based on Support Vector Machine and Logistic Regression Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rongyuan Qin
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Regression Model ◽  
Capital Market ◽  
Logistic Regression Model ◽  
Learning Ability ◽  
Support Vector ◽  
Accounting Fraud ◽  
Model Setting ◽  
The Capital Market

The authenticity of the company’s accounting information is an important guarantee for the effective operation of the capital market. Accounting fraud is the tampering and distortion of the company’s public disclosure information. The continuous outbreak of fraud cases has dealt a heavy blow to the confidence of investors, shaken the credit foundation of the capital market, and hindered the healthy and stable development of the capital market. Therefore, it is of great theoretical and practical significance to carry out the research on the identification and governance of accounting fraud. Traditionally, accounting fraud identification is mostly based on linear thinking to build the fraud identification model. However, more and more studies show that fraud has typical nonlinear characteristics, and the multiobjective of fraud means also determines the limitations of using the linear model for identification. Considering that the traditional identification methods may have the defects of model setting error and insufficient information extraction, this paper constructs the support vector machine and logistic regression model to identify accounting fraud. The support vector machine is used to improve the learning ability and generalization ability of unknown phenomena, and the explanatory power of each variable to the whole model is identified by the logistic regression model. This paper breaks through the linear constraint hypothesis and explores the model setting form which is more suitable for the law of corporate fraud behaviour to extract the fraud identification information more fully and provide more powerful support for investors to effectively identify fraud.

Prediction of chronic kidney disease using urinary dielectric properties and support vector machine

2016 ◽  
Vol 50 (3) ◽  
pp. 201-213 ◽  
Author(s):  
Peck Shen Mun ◽  
Hua Nong Ting ◽  
Seyed Mostafa Mirhassani ◽  
Teng Aik Ong ◽  
Chew Ming Wong ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Support Vector Machine ◽  
Dielectric Properties ◽  
Kidney Disease ◽  
Support Vector

scholarly journals Prediction of all-cause mortality in coronary artery disease patients with atrial fibrillation based on machine learning models

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xinyun Liu ◽  
Jicheng Jiang ◽  
Lili Wei ◽  
Wenlu Xing ◽  
Hailong Shang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Coronary Artery Disease ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Support Vector ◽  
Vector Machines ◽  
All Cause Mortality ◽  
Artery Disease

Abstract Background Machine learning (ML) can include more diverse and more complex variables to construct models. This study aimed to develop models based on ML methods to predict the all-cause mortality in coronary artery disease (CAD) patients with atrial fibrillation (AF). Methods A total of 2037 CAD patients with AF were included in this study. Three ML methods were used, including the regularization logistic regression, random forest, and support vector machines. The fivefold cross-validation was used to evaluate model performance. The performance was quantified by calculating the area under the curve (AUC) with 95% confidence intervals (CI), sensitivity, specificity, and accuracy. Results After univariate analysis, 24 variables with statistical differences were included into the models. The AUC of regularization logistic regression model, random forest model, and support vector machines model was 0.732 (95% CI 0.649–0.816), 0.728 (95% CI 0.642–0.813), and 0.712 (95% CI 0.630–0.794), respectively. The regularization logistic regression model presented the highest AUC value (0.732 vs 0.728 vs 0.712), specificity (0.699 vs 0.663 vs 0.668), and accuracy (0.936 vs 0.935 vs 0.935) among the three models. However, no statistical differences were observed in the receiver operating characteristic (ROC) curve of the three models (all P > 0.05). Conclusion Combining the performance of all aspects of the models, the regularization logistic regression model was recommended to be used in clinical practice.

scholarly journals Ensemble swarm behaviour based feature selection and support vector machine classifier for chronic kidney disease prediction

2018 ◽  
Vol 7 (2.31) ◽  
pp. 190 ◽  
Author(s):  
S Belina V.J. Sara ◽  
K Kalaiselvi
Keyword(s):  
Chronic Kidney Disease ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Kidney Disease ◽  
Information Gain ◽  
Clonal Selection ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Algorithms

Kidney Disease and kidney failure is the one of the complicated and challenging health issues regarding human health. Without having any symptoms few diseases are detected in later stages which results in dialysis. Advanced excavating technologies can always give various possibilities to deal with the situation by determining important realations and associations in drilling down health related data.   The prediction accuracy of classification algorithms depends upon appropriate Feature Selection (FS) algorithms decrease the number of features from collection of data. FS is the procedure of choosing the most relevant features, removing irrelevant features. To identify the Chronic Kidney Disease (CKD), Hybrid Wrapper and Filter based FS (HWFFS) algorithm is proposed to reduce the dimension of CKD dataset.   Filter based FS algorithm is performed based on the three major functions: Information Gain (IG), Correlation Based Feature Selection (CFS) and Consistency Based Subset Evaluation (CS) algorithms respectively. Wrapper based FS algorithm is performed based on the Enhanced Immune Clonal Selection (EICS) algorithm to choose most important features from the CKD dataset.  The results from these FS algorithms are combined with new HWFFS algorithm using classification threshold value.  Finally Support Vector Machine (SVM) based prediction algorithm be proposed in order to predict CKD and being evaluated on the MATLAB platform. The results demonstrated with the purpose of the SVM classifier by using HWFFS algorithm provides higher prediction rate in the diagnosis of CKD when compared to other classification algorithms.  

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