A Proposed Stacked Machine Learning Model to Predict the Survival of a Patient with Heart Failure

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Author(s)

Md. Raihan Mahmud 1,* Dip Nandi 1 Md. Shamsur Rahim 2 Christe Antora Chowdhury 3

1. American International University-Bangladesh, 408/1 (Old KA 66/1), Kuratoli, Khilkhet, Dhaka 1229, Bangladesh

2. School of Computer Science, University of Technology Sydney, Australia

3. Popular Medical College, 25 Road no 2, Dhanmondi, Dhaka 1205, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2024.03.03

Received: 2 Aug. 2023 / Revised: 5 Jan. 2024 / Accepted: 1 Mar. 2024 / Published: 8 Jun. 2024

Index Terms

Machine Learning, Heart Failure, Stacked Machine Learning Model, Scikit-learn

Abstract

Now a days heart failure is one of the most common chronic diseases that cause death. As it possesses high risk of death, it is important to predict patient’s survival and optimize treatment strategies. Machine learning techniques have come to light as useful tools for evaluating enormous quantities of patient data and deriving important patterns and insights in recent years. The purpose of the study is to investigate the feasibility of using the machine learning methods for predicting heart failure patient’s chances of survival. We have worked on a dataset with 2029 heart failure patients and the dataset comprises 13 features. To conduct this research, we suggested a model (Stacked machine learning model using scikit-learn using Decision Tree, Naive Bias, Random Forest, Linear Regression, SVM, XGBoost, ANN) using which we got better results than previously existed researches. We believe the suggested model will help advance our understanding of heart attack prediction.

Cite This Paper

Md. Raihan Mahmud, Dip Nandi, Md. Shamsur Rahim, Christe Antora Chowdhury, "A Proposed Stacked Machine Learning Model to Predict the Survival of a Patient with Heart Failure", International Journal of Intelligent Systems and Applications(IJISA), Vol.16, No.3, pp.35-46, 2024. DOI:10.5815/ijisa.2024.03.03

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