Search Articles

View query in Help articles search
e.g. John Smith or 0000-0001-6479-5330

Search Results (1 to 5 of 5 Results)

Download search results: CSV END BibTex RIS

Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study

Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study

For a set of {xi, yi}, i = 1, …, N, xi ∈ Rdyi ∈ {+1, –1}, the SVM found a vector ω such that yi (ωTxi – b) > 0. The vector split the data into 2 classes. Many lines were available for splitting the set. The SVM optimized the solution by solving: And retrieved the solution from: The RF algorithm was based on bagging and decision [15]. Bootstrap aggregating (bagging) used repeated random sampling and replaced the training set to create a subset, reduce variance, and improve accuracy.

Po-Yuan Su, Yi-Chia Wei, Hao Luo, Chi-Hung Liu, Wen-Yi Huang, Kuan-Fu Chen, Ching-Po Lin, Hung-Yu Wei, Tsong-Hai Lee

JMIR Med Inform 2022;10(3):e32508

Download Citation: END BibTex RIS

Download PDF Download XML
Article has an altmetric score of 2