pone.0298036.s001.pdf (86.74 kB)
The rates of missing data for each feature used in this study.
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posted on 2024-02-15, 18:30 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 SongThe rates of missing data for each feature used in this study.
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stacked ensemble learningnet reclassification indexgeneralized linear modelextreme gradient boostingemploy machine learningelevation myocardial infarction40 82112006 8211enhanced risk stratificationalgorithm performance achievedua asian cohortasian patients diagnosedtimi score tendsdata analytics approachbest ml algorithm2019 ), representingkey features identifiedua risk scoretimi risk scorestacked el modelseparate validation datasetasians palgorithm development utilizedterm mortality followingua patients acrossxlink "> usingml algorithms holdsxlink ">term mortalitycardiovascular riskua usingpredictive performancemortality predictionsclassifying patientsassessed using518 patients133 patients031 patientsyear datarf ),nb ),ml allowsanalyzed datasignificant featuresreduced featuresclinical featuresassociated featuresyear predictionsweight heparinunstable anginasubsequently evaluatingrandom forestprecise identificationperiods yieldedmeta learnerkillip classincluding demographicheart rateethnic populationcontinuous developmentbroad multibackward elimination
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