Retrograde flow in aortic isthmus in normal and fetal heart disease by principal component analysis and computational fluid dynamics

2022 ◽  
Author(s):  
Zhuo Chen ◽  
Hongkai Zhao ◽  
Ying Zhao ◽  
Jiancheng Han ◽  
Xu Yang ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Principal Component Analysis ◽  
Computational Fluid Dynamics ◽  
Heart Disease ◽  
Fetal Heart ◽  
Principal Component ◽  
Component Analysis ◽  
Retrograde Flow ◽  
Aortic Isthmus

scholarly journals Feature Scaled Element Balancing with Random Boosting for Heart Disease Prediction using Machine Learning

2020 ◽  
Vol 8 (5) ◽  
pp. 4105-4110
Keyword(s):  
Principal Component Analysis ◽  
Heart Disease ◽  
Naive Bayes ◽  
Principal Component ◽  
Component Analysis ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Data Set

In the current scenario, the researchers are focusing towards health care project for the prediction of the disease and its type. In addition to the prediction, there exists a need to find the influencing parameter that directly related to the disease prediction. The analysis of the parameters needed to the prediction of the disease still remains a challenging issue. With this view, we focus on predicting the heart disease by applying the dataset with boosting the parameters of the dataset. The heart disease data set extracted from UCI Machine Learning Repository is used for implementation. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data preprocessing is done and the attribute relationship is identified by the correlation values. Second, the data set is fitted to random boost regressor and the important features are identified. Third, the dataset is feature scaled reduced and then fitted to random forest classifier, decision tree classifier, Naïve bayes classifier, logistic regression classifier, kernel support vector machine and KNN classifier. Fourth, the dataset is reduced with principal component analysis with five components and then fitted to the above mentioned classifiers. Fifth, the performance of the classifiers is analyzed with the metrics like accuracy, recall, fscore and precision. Experimental results shows that, the Naïve bayes classifier is more effective with the precision, Recall and Fscore of 0.89 without random boost, 0.88 with random boosting and 0.90 with principal component analysis. Experimental results show, the Naïve bayes classifier is more effective with the accuracy of 89% without random boost, 90% with random boosting and 91% with principal component analysis.

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

2017 ◽  
Vol 482 ◽  
pp. 796-807 ◽  
Author(s):  
Syed Muhammad Saqlain Shah ◽  
Safeera Batool ◽  
Imran Khan ◽  
Muhammad Usman Ashraf ◽  
Syed Hussnain Abbas ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Heart Disease ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Diagnosis ◽  
Heart Disease Diagnosis ◽  
Probabilistic Principal Component Analysis

scholarly journals Study of risk factors for healthcare-associated infections in acute cardiac patients using categorical principal component analysis (CATPCA)

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Emilio Renes Carreño ◽  
Almudena Escribá Bárcena ◽  
Mercedes Catalán González ◽  
Francisco Álvarez Lerma ◽  
Mercedes Palomar Martínez ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Heart Disease ◽  
Principal Component ◽  
Component Analysis ◽  
Apache Ii Score ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Icu Stay ◽  
Categorical Principal Component Analysis ◽  
Using Data

AbstractUsing categorical principal component analysis, we aimed to determine the relationship between health care-associated infections (HAIs) and diagnostic categories (DCs) in patients with acute heart disease using data collected in the Spanish prospective ENVIN-HELICS intensive care registry over a 10-year period (2005–2015). A total of 69,876 admissions were included, of which 5597 developed HAIs. Two 2-component CATPCA models were developed. In the first model, all cases were included; the first component was determined by the duration of the invasive devices, the ICU stay, the APACHE II score and the HAIs; the second component was determined by the type of admission (medical or surgical) and by the DCs. No clear association between DCs and HAIs was found. Cronbach’s alpha was 0.899, and the variance accounted for (VAF) was 52.5%. The second model included only admissions that developed HAIs; the first component was determined by the duration of the invasive devices and the ICU stay; the second component was determined by the inflammatory response, the mortality in the ICU and the HAIs. Cronbach’s alpha value was 0.855, and VAF was 46.9%. These findings highlight the role of exposure to invasive devices in the development of HAIS in patients with acute heart disease.

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