Comparison on the Application of BP Neural Network and Logistic Regression in the Study of Technology Life Cycle

The arrival of the era of big data has brought new ideas to solve problems for all walks of life. Medical clinical data is collected and stored in the medical field by utilizing the medical big data platform. Based on medical information big data, new ideas and methods for the differential diagnosis of hypo-MDS and AA are studied. The basic information, peripheral blood classification counts, peripheral blood cell morphology, bone marrow cell morphology, and other information were collected from patients diagnosed with hypo-MDS and AA diagnosed in the first diagnosis. First, statistical analysis was performed. Then, the logistic regression model, decision tree model, BP neural network model, and support vector machine (SVM) model of hypo-MDS and AA were established. The sensitivity, specificity, Youden index, positive likelihood ratio (+LR), negative likelihood ratio (−LR), area under curve (AUC), accuracy, Kappa value, positive predictive value (+PV), negative predictive value (−PV) of the four model training set and test set were compared, respectively. Finally, with the support of medical big data, using logistic regression, decision tree, BP neural network, and SVM four classification algorithms, the decision tree algorithm is optimal for the classification of hypo-MDS and AA and analyzes the characteristics of the optimal model misjudgment data.

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Classification of heart sound signals with BP neural network and logistic regression

2017 Chinese Automation Congress (CAC) ◽

10.1109/cac.2017.8244111 ◽

2017 ◽

Author(s):

Lina Li ◽

Xinpei Wang ◽

Xiaping Du ◽

Yuanyuan Liu ◽

Changchun Liu ◽

...

Keyword(s):

Neural Network ◽

Logistic Regression ◽

Bp Neural Network ◽

Heart Sound

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Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

10.21203/rs.3.rs-390569/v1 ◽

2021 ◽

Author(s):

Li Lu Wei ◽

Yu jian

Keyword(s):

Neural Network ◽

Risk Factors ◽

Logistic Regression ◽

Regression Model ◽

Bp Neural Network ◽

Logistic Regression Model ◽

Classification Tree ◽

Overweight And Obesity ◽

Tree Model ◽

Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

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Life Cycle Cost Estimated for the Diesel in Mechanical Engineering Based on the BP Neural Network

Advances in Mechanical and Electronic Engineering - Lecture Notes in Electrical Engineering ◽

10.1007/978-3-642-31507-7_34 ◽

2012 ◽

pp. 203-208

Author(s):

JiaShan Jin ◽

JunBao Geng ◽

LinKai Sun ◽

GuoWei Chen

Keyword(s):

Neural Network ◽

Life Cycle ◽

Bp Neural Network ◽

Life Cycle Cost ◽

Mechanical Engineering

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Comparison of BP Neural Network Model and Logistic Regression in the Analysis of Influencing Factors of Violence in Hospitals

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.50-51.964 ◽

2011 ◽

Vol 50-51 ◽

pp. 964-967 ◽

Cited By ~ 2

Author(s):

Jian Hui Wu ◽

Guo Li Wang ◽

Xiao Ming Li ◽

Su Feng Yin

Keyword(s):

Neural Network ◽

Logistic Regression ◽

Network Model ◽

Neural Network Model ◽

Roc Curve ◽

Bp Neural Network ◽

Logistic Regression Model ◽

Training Set ◽

Hospital Violence ◽

Bp Neural Network Model

Collecting violence cases for medical personnel from different levels of the hospital of Tangshan, we create a model for influential factors of hospital violence, and respectively with BP Nerve Network and logistic regression, by sensitivity, specificity and ROC curve, it is compared with two methods,in order to discovering effective analytical method . The training set and testing set sensitivity of BP Neural Network Model are 0.916 and 0.935,and the specificity is 0.447 and 0.526,the area of ROC curve is 0.769 and 0.785；for logistic regression Model ,for its the training set and testing set, sensitivity is 0.907 and 0.925, the specificity is 0.377 and 0.404, the area of ROC curve is 0.663and0.666. In hospital violence influencing factors, the forecast capability of BP Neural Network Model is better than logistic regression Model and it has farther extend value.

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