Effective Prediction of Mortality by Heart Disease among Women Using CHAID Model: Validation Study
ABSTRACT
Background:
Many current studies claimed that the actual risk of heart disease among women is equal to those of men.
Objective:
This study aims to predict mortality caused by heart diseases among women using machine learning algorithms as an artificial intelligence (AI) technique
Methods:
A retrospective design in retrieving big data from the electronic health records (EHRs) for 2,028 women with heart diseases was used. Data were collected for Jordanian women who were admitted to public health hospitals from the year 2015 to the end of 2021
Results:
Out of nine AI models, the Chi-squared Automatic Interaction Detection (CHAID) model proved the highest accuracy (93.25) and area under the curve of 0.825 among the build models. The main predictors for death among women were pulse oximetry, pulse, age, systolic blood pressure readings, medical diagnosis, and pulse pressure.
Conclusions:
The CHAID model as a machine learning algorithm helps in the identification of the main predictors of cardiovascular mortality among women and can be used as a convenient tool for clinical prediction.
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