two-way hierarchical cluster (heat map) analysis  (OriginLab corp)

Journal: Scientific Reports

Article Title: Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction

doi: 10.1038/s41598-024-82184-2

Figure Lengend Snippet: Pearson heat map after UCI data augmentation.

Article Snippet: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho = \frac{Cov(X,Y)}{{\sigma_{X} \sigma_{Y} }}$$\end{document} In order to show the one-hot encoded data in detail, so as to determine whether there is a need for subsequent dimensionality reduction, this paper conducted Pearson heat map correlation analysis on UCI data and Kaggle data.

Techniques:

Journal: Scientific Reports

Article Title: Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction

doi: 10.1038/s41598-024-82184-2

Figure Lengend Snippet: Pearson heat map after Kaggle data augmentation.

Article Snippet: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho = \frac{Cov(X,Y)}{{\sigma_{X} \sigma_{Y} }}$$\end{document} In order to show the one-hot encoded data in detail, so as to determine whether there is a need for subsequent dimensionality reduction, this paper conducted Pearson heat map correlation analysis on UCI data and Kaggle data.

Techniques: