pone.0298036.g005.tif (397.03 kB)

TIMI risk score’s mortality rate distribution for NSTEMI/UA patients on the validation set.

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posted on 2024-02-15, 18:31 authored by Sazzli Kasim, Putri Nur Fatin Amir Rudin, Sorayya Malek, Firdaus Aziz, Wan Azman Wan Ahmad, Khairul Shafiq Ibrahim, Muhammad Hanis Muhmad Hamidi, Raja Ezman Raja Shariff, Alan Yean Yip Fong, Cheen Song

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stacked ensemble learning net reclassification index generalized linear model extreme gradient boosting employ machine learning elevation myocardial infarction 40 &# 8211 2006 &# 8211 enhanced risk stratification algorithm performance achieved ua asian cohort asian patients diagnosed timi score tends data analytics approach best ml algorithm 2019 ), representing key features identified ua risk score timi risk score stacked el model separate validation dataset asians algorithm development utilized term mortality following ua patients across xlink "> using ml algorithms holds xlink ">term mortality cardiovascular risk ua using predictive performance mortality predictions classifying patients assessed using 518 patients 133 patients 031 patients year data rf ),nb ),ml allows analyzed data significant features reduced features clinical features associated features year predictions weight heparin unstable angina subsequently evaluating random forest precise identification periods yielded meta learner killip class including demographic heart rate ethnic population continuous development broad multi backward elimination

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TIMI risk score’s mortality rate distribution for NSTEMI/UA patients on the validation set.

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