Scholarlink

Machine Learning helps drive advances in the development of artificial intelligence, as a human may no longer be able to fully and accurately interpret massive data, especially in predicting heart disease. The study aimed at predicting the survival of patients who underwent an echocardiography procedure using data mining techniques. The researchers utilized a descriptive-correlation and analytical research design. Data dining techniques were utilized using Python programming. There are 2,200 two-dimensional (2D) echocardiography datasets. The results indicated that Random Forest performs well in predicting survival among all other algorithms: Logistic Regression, Naïve Bayes, and Gradient Boosting Classifier and Extra Tree Classifier with ROC of 76.91%, and Left Ventricle’s End Diastolic Diameter (LVEDD) has significant relationship to increased risk or a decreased of survival time.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Comparative Analysis of Machine Learning Models for Predicting Patient Survival in Echocardiography Procedures

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Darrel A. Cardaña,
  • Daniel D. Dasig,
  • Diadem Jane C. Maglangit

摘要

Machine Learning helps drive advances in the development of artificial intelligence, as a human may no longer be able to fully and accurately interpret massive data, especially in predicting heart disease. The study aimed at predicting the survival of patients who underwent an echocardiography procedure using data mining techniques. The researchers utilized a descriptive-correlation and analytical research design. Data dining techniques were utilized using Python programming. There are 2,200 two-dimensional (2D) echocardiography datasets. The results indicated that Random Forest performs well in predicting survival among all other algorithms: Logistic Regression, Naïve Bayes, and Gradient Boosting Classifier and Extra Tree Classifier with ROC of 76.91%, and Left Ventricle’s End Diastolic Diameter (LVEDD) has significant relationship to increased risk or a decreased of survival time.