Risk factors in BSH: ROC analysis using logistic regression.

<p>Panels A is a forest plot of logistic regression, displaying the quantity, odds ratio (OR), and P-value of various factors associated with BSH (e.g., age, BMI). This visualization helps assess the degree of influence each factor has on the disease. Panels B is the receiver operating charact...

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Bibliographic Details
Main Author:	Lei Gao (6566) (author)
Other Authors:	Boyan Tian (21627172) (author), Qiqi Jia (11900763) (author), Xingyu He (3874243) (author), Guannan Zhao (618195) (author), Yueheng Wang (13908540) (author)
Published:	2025
Subjects:	Biotechnology Developmental Biology Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified shapley additive explanation recognized morphological change nearest neighbor classification mitral valve peak interpretable machine learning first central hospital common echocardiographic finding capturing nonlinear relationships 947 &# 8211 93 &# 8211 831 &# 8211 764 &# 8211 023 &# 8211 021 &# 8211 018 &# 8211 basal septal hypertrophy 998 ), 0 949 ), 1 888 ), 1 403 ), 1 064 ), 0 052 ), 0 xlink "> however xlink "> echocardiographic ao angle identified ao angle emerging relevant variable ivs machine learning algorithms effectively predict bsh decision curve analysis sd ), glu dt ), k dbp ), distance 987 ), respectively rf model yielded xlink "> ao angle 281 ), basal segment knn ), yet efficient traditional algorithms top four test sets test set tavi ). significant cause roc curve rf model random forest
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Risk factors in BSH: ROC analysis using logistic regression.

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