Development of Heavy Rain Damage Prediction Function Using Principal Component Analysis and Logistic Regression Model

2017 ◽  
Vol 17 (6) ◽  
pp. 159-166 ◽  
Author(s):  
Changhyun Choi ◽  
◽  
Jongsung Kim ◽  
Myungjin Lee ◽  
Jeonghwan Kim ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Principal Component ◽  
Heavy Rain ◽  
Component Analysis ◽  
Damage Prediction ◽  
Prediction Function

Application of Principal Component Analysis and Logistic Regression Model in Lupus Nephritis Patients with Clinical Hypothyroidism

2020 ◽  
Author(s):  
Ting Huang ◽  
Jiarong Li ◽  
Weiru Zhang
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
Logistic Regression ◽  
Lupus Nephritis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Principal Component ◽  
Component Analysis ◽  
Cross Sectional

Abstract Background : Previous studies indicate that the prevalence of hypothyroidism is much higher in patients with lupus nephritis (LN) than in the general population, and is associated with LN’s activity. Principal component analysis (PCA) and logistic regression can help determine relevant risk factors and identify LN patients at high risk of hypothyroidism; as such, these tools may prove useful in managing this disease. Methods: We carried out a cross-sectional study of 143 LN patients diagnosed by renal biopsy, all of whom had been admitted to Xiangya Hospital of Central South University in Changsha, China, between June 2012 and December 2016. The PCA–logistic regression model was used to determine the influential principal components for LN patients who have hypothyroidism. Results : Our PCA–logistic regression analysis results demonstrated that serum creatinine, blood urea nitrogen, blood uric acid, total protein, albumin, and anti-ribonucleoprotein antibody were important clinical variables for LN patients with hypothyroidism. The area under the curve of this model was 0.855. Conclusion : The PCA–logistic regression model performed well in identifying important risk factors for certain clinical outcomes, and promoting clinical research on other diseases will be beneficial. Using this model, clinicians can identify at-risk subjects and either implement preventative strategies or manage current treatments.

scholarly journals Principal component analysis optimization of a PM2.5 land use regression model with small monitoring network

2012 ◽  
Vol 425 ◽  
pp. 27-34 ◽  
Author(s):  
Hector A. Olvera ◽  
Mario Garcia ◽  
Wen-Whai Li ◽  
Hongling Yang ◽  
Maria A. Amaya ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Land Use ◽  
Regression Model ◽  
Monitoring Network ◽  
Principal Component ◽  
Component Analysis ◽  
Land Use Regression

Objective Categorization of Heavy-Rain-Producing MCS Synoptic Types by Rotated Principal Component Analysis

2014 ◽  
Vol 142 (5) ◽  
pp. 1716-1737 ◽  
Author(s):  
John M. Peters ◽  
Russ S. Schumacher
Keyword(s):  
Principal Component Analysis ◽  
Temperature Gradient ◽  
Potential Temperature ◽  
Warm Season ◽  
Principal Component ◽  
Heavy Rain ◽  
Component Analysis ◽  
Synoptic Type ◽  
Low Level ◽  
Upper Level

Abstract In this research, rotated principal component analysis was applied to the atmospheric fields associated with a large sample of heavy-rain-producing mesoscale convective systems (MCSs). Cluster analysis in the subspace defined by the leading two resulting principal components revealed two subtypes with distinct synoptic and mesoscale characteristics, which are referred to as warm-season-type and synoptic-type events, respectively. Subsequent composite analysis showed that both subtypes typically occurred on the cool side of a quasi-stationary, low-level frontal boundary, within a region of locally maximized low-level convergence and warm advection. Synoptic-type events, which tended to exhibit greater horizontal extent than warm-season-type events, typically occurred downstream of a progressive upper-level trough, along a low-level potential temperature gradient with the warmest air to the south and southeast. Warm-season-type events, on the other hand, occurred within the right-entrance region of a minimally to anticyclonically curved upper-level jet streak, along a low-level potential temperature gradient with the warmest low-level air to the southwest. Synoptic-scale forcing for ascent was stronger in synoptic-type events, while low-level moisture was greater in warm-season-type events. Warm-season-type events were frequently preceded by the passage of a trailing-stratiform- (TS) type MCS, whereas synoptic-type events often occurred prior to the passage of a TS-type system. Analysis of the composite vertical wind profiles at the event location suggests that quasi-stationary behavior in warm-season events predominantly resulted from upstream propagation that nearly canceled advection by the mean steering flow, whereas in the case of synoptic-type events training predominantly resulted from system motion that paralleled a front.

scholarly journals Performance Analysis of Classifiers in Identifying NREM Sleep and Awaken Stages from EEG Signals

2020 ◽  
Vol 8 (6) ◽  
pp. 4321-4326
Keyword(s):  
Principal Component Analysis ◽  
Logistic Regression ◽  
Linear Regression ◽  
Expectation Maximization ◽  
Principal Component ◽  
Nrem Sleep ◽  
Component Analysis ◽  
Medical Procedure ◽  
Eeg Signals ◽  
Non Linear

Electroencephalogram is a medical procedure which helps in analyzing the activities of the brain through electrical signals. In this paper a simple classification technique of EEG signal into two stages as NREM sleep and awaken stages had been undertaken. Classifying these stages helps the physician to understand the patient's sleep disorder by knowing whether the person's brain is in NREM sleep or awaken stages. Physionet EEG signals are samples of 256 signals per second for 10 seconds duration is used in this work. Then the EEG samples properties are analyzed through various parameters like statistical features, entropy Pearson correlation coefficient, Power spectral density, scatter plots and Hilbert transform plots. The classification of NREM sleep and awaken stage is performed by the ten different classifiers broadly grouped into non linear and hybrid one. The classifiers used include Linear Regression, Non Linear Regression, Logistic Regression, Principal Component Analysis, Kernel Principal Component Analysis, Expectation Maximization, Compensatory Expectation Maximization, Expectation Maximization with Logistic Regression Compensatory Expectation Maximization with Logistic Regression, and Firefly. The performances of the classifiers are analyzed using regular parameters like sensitivity, accuracy, specificity, performance index. The highest accuracy of 95.575% is achieved with linear regression for awaken signal and an accuracy of 95.315% is achieved using kernel PCA for sleep signal.

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