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Predicting mortality amongst Jordanian men with heart attacks using the chi-square automatic interaction detection

Salam Bani Hani1, Muayyad Ahmad2

  • 1School of Nursing, Nursing Department, Irbid National University, Irbid, Jordan.

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|August 8, 2024
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Summary

This study developed a machine learning model to predict heart attack mortality in men. The Chi-Squared Automated Interactive Detection (CHAID) model achieved 93.72% accuracy, identifying key risk factors for better patient outcomes.

Keywords:
acute coronary syndromeartificial intelligencechi-square automatic interaction detectionheart attackmachine learning algorithmsprediction

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Area of Science:

  • Cardiovascular Disease Research
  • Machine Learning in Healthcare
  • Public Health Informatics

Background:

  • Heart attack is a leading global cardiovascular disease.
  • Men are disproportionately affected by cardiac conditions.
  • Accurate prediction of heart attack outcomes is crucial for saving lives.

Purpose of the Study:

  • To propose and evaluate the Chi-Squared Automated Interactive Detection (CHAID) model for predicting mortality in men experiencing heart attacks.
  • To identify significant risk factors for heart attack mortality in a Jordanian male population.

Main Methods:

  • Retrospective, predictive study design using data from Jordan's electronic health solution system (2015-2021).
  • Information gathered from male patients admitted to public hospitals.
  • Application of the CHAID algorithm for predictive modeling.

Main Results:

  • The CHAID model demonstrated high predictive accuracy (93.72%) and an Area Under the Curve (AUC) of 0.792.
  • Key predictors of mortality included governorates, age, pulse oximetry, medical diagnosis, pulse pressure, heart rate, and systolic blood pressure.
  • The CHAID model was identified as the top-performing model for predicting mortality in this cohort.

Conclusions:

  • Demographic characteristics and hemodynamic readings are significant predictors of heart attack mortality.
  • Machine learning algorithms, specifically the CHAID model, can effectively predict mortality risk in heart attack patients.
  • The findings highlight the utility of the CHAID model for improving patient outcomes in cardiovascular disease management.