International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 16, No. 3, 8 Jun. 2024
Cover page and Table of Contents: PDF (size: 588KB)
A Proposed Stacked Machine Learning Model to Predict the Survival of a Patient with Heart Failure
PDF (588KB), PP.35-46
Views: 0 Downloads: 0
Author(s)
Md. Raihan Mahmud
1,*
Dip Nandi
1
Md. Shamsur Rahim
2
Christe Antora Chowdhury
3
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
Reference
[1]Moreno-Sanchez, P. A., "Development of an Explainable Prediction Model of Heart Failure Survival by Using Ensemble Trees," 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 4902-4910, doi: 10.1109/BigData50022.2020.9378460
[2]Mbotwa, J., Kamps, M. D., Baxter, P. D., Gilthorpe, M., "Application of Cox Model to predict the survival of patients with Chronic Heart Failure: A latent class regression approach." arXiv preprint arXiv:1907.07957 (2019).
[3]Haque, M. E., Uddin, S., Islam, M. A., Khanom, A., Suman, A., Paul, M., "Analysis and prediction of heart stroke from ejection fraction and serum creatinine using LSTM deep learning approach." arXiv preprint arXiv:2209.13799 (2022).
[4]David, H. B. F., Belcy. S. A., "HEART DISEASE PREDICTION USING DATA MINING TECHNIQUES." ICTACT Journal on Soft Computing 9, no. 1 (2018).
[5]Yang, X., Gong, Y., Waheed, N., March, K., Bian, J., Hogan, W. R., Wu, Y., "Identifying cancer patients at risk for heart failure using machine learning methods." In AMIA Annual Symposium Proceedings, vol. 2019, p. 933. American Medical Informatics Association, 2019.
[6]Dong, S., Mutharasan, R., Jonnalagadda, S. R., "Using Natural Language Processing to Screen Patients with Active Heart Failure: An Exploration for Hospital-wide Surveillance." arXiv preprint arXiv:1609.01580 (2016).
[7]Jabbar, M. A., Deekshatulu, B. L., Chandra, P., "Heart disease prediction using lazy associative classification." In 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), pp. 40-46. IEEE, 2013.
[8]Roadknight, C., Suryanarayanan, D., Aickelin, U., Scholefield, J., Durrant, L., "An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates." In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1-8. IEEE, 2015.
[9]Dev, S., Wang, H., Nwosu, C. S., Jain, N., Veeravalli, B., John, D., "A predictive analytics approach for stroke prediction using machine learning and neural networks." Healthcare Analytics 2 (2022): 100032.
[10]Yin, Y., Chou, C., "Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure." arXiv preprint arXiv:2109.03048 (2021).
[11]Chicco, D., Jurman, G., "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone." BMC medical informatics and decision making 20, no. 1 (2020): 1-16.
[12]Chicco, D., Jurman, G., "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation." BMC genomics 21, no. 1 (2020): 1-13.
[13]Chicco, D., Rovelli, C., "Computational prediction of diagnosis and feature selection on mesothelioma patient health records." PloS one 14, no. 1 (2019): e0208737.
[14]Patel, J., TejalUpadhyay., Patel, S., "Heart disease prediction using machine learning and data mining technique." Heart Disease 7, no. 1 (2015): 129-137.
[15]Erdaş, Ç. B., Ölçer. D., "A machine learning-based approach to detect survival of heart failure patients." In 2020 Medical Technologies Congress (TIPTEKNO), pp. 1-4. IEEE, 2020.
[16]Ishaq, A., Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Rupapara, V., Nappi, M., "Improving the prediction of heart failure patients’ survival using SMOTE and effective data mining techniques." IEEE access 9 (2021): 39707-39716.
[17]Alotaibi, F. S. "Implementation of machine learning model to predict heart failure disease." International Journal of Advanced Computer Science and Applications 10, no. 6 (2019).