Explainable Prediction of Acute Myocardial Infarction Using Machine Learning and Shapley Values
The early and accurate detection of the onset of acute myocardial infarction (AMI) is imperative for the timely provision of medical intervention and the reduction of its mortality rate. Machine learning techniques have demonstrated great potential in aiding disease diagnosis. In this paper, we pres...
Main Authors: | Lujain Ibrahim, Munib Mesinovic, Kai-Wen Yang, Mohamad A. Eid |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9268965/ |
Similar Items
-
ATYPICAL CLINICAL COURSE OF THE ACUTE MYOCARDIAL INFARCTION: CLINICAL AND ANAMNESTIC CHARACTERISTIC OF PATIENTS, MANAGEMENT AND OUTCOMES (DATA FROM REGISTRY OF ACUTE MYOCARDIAL INFARCTION)
by: A. А. Garganeeva, et al.
Published: (2016-08-01) -
Derivation and validation of a prediction score for acute kidney injury secondary to acute myocardial infarction in Chinese patients
by: Feng-bo Xu, et al.
Published: (2019-05-01) -
Machine Learning to Predict the 1-Year Mortality Rate After Acute Anterior Myocardial Infarction in Chinese Patients
by: Li Y, et al.
Published: (2020-01-01) -
Machine Learning to Predict the 1-Year Mortality Rate After Acute Anterior Myocardial Infarction in Chinese Patients [Corrigendum]
by: Li Y, et al.
Published: (2020-01-01) -
Ability of the LACE Index to Predict 30-Day Readmissions in Patients with Acute Myocardial Infarction
by: Han, W., et al.
Published: (2022)