Stacking Ensemble Machine Learning Algorithm with an Application to Heart Disease Prediction

Authors

  • Ruhi Fatima Department of Computer Science, College of Computer and Information Sciences, Majmaah University, AlMajmaah, 11952, Saudi Arabia
  • Sabeena Kazi Department of Basic Sciences and Humanities, College of Computer and Information Sciences, Majmaah University, AlMajmaah, 11952, Saudi Arabia
  • Asifa Tassaddiq Department of Basic Sciences and Humanities, College of Computer and Information Sciences, Majmaah University, AlMajmaah, 11952, Saudi Arabia
  • Nilofer Farhat Department of Computer Science, College of Computer and Information Sciences, Majmaah University, AlMajmaah, 11952, Saudi Arabia
  • Humera Naaz Department of Basic Sciences and Humanities, College of Computer and Information Sciences, Majmaah University, AlMajmaah, 11952, Saudi Arabia
  • Sumera Jabeen Department of Computer Science Engineering, CMR Engineering College, Hyderabad, 501401, India

DOI:

https://doi.org/10.37256/cm.4420232390

Keywords:

classifier methods, heart disease, machine learning, prediction, metrics

Abstract

Mathematics and statistics have a significant impact on the advancement of most trending sciences like machine learning, artificial intelligence, and data science. In this article, we use the Stacking Ensemble Machine Learning Algorithm (SEMLA) to predict heart disease, considering accuracy (acc), diagnostic odds ratio (Dor), F1_score, Matthews correlation coefficient (Mcc), receiver operating characteristics-area under curve (roc-auc), and logloss (log_loss). The data is analyzed using classification learning techniques. We have considered sex, age, cholesterol, fasting blood sugar, the highest rate of heartbeat, type of chest pain, resting electrocardiogram (ECG), angina, depression induced by exercise, peak exercise measurement, major vessel number, a disorder in the blood, and a target attribute to represent the presence and absence of disorders. The approach used allows for the prediction of heart disease and the management of worst-case scenarios. In comparison with the existing models, our proposed model has outperformed other models with an accuracy of 97.28%.

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Published

2023-11-06

How to Cite

1.
Fatima R, Kazi S, Tassaddiq A, Farhat N, Naaz H, Jabeen S. Stacking Ensemble Machine Learning Algorithm with an Application to Heart Disease Prediction. Contemp. Math. [Internet]. 2023 Nov. 6 [cited 2024 May 28];4(4):905-2. Available from: https://ojs.wiserpub.com/index.php/CM/article/view/2390

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