Improving the Prediction of Heart Failure Patients' Survival Using SMOTE and Effective Data Mining Techniques

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Ishaq, Abid
Sadiq, Saima
Umer, Muhammad
Ullah, Saleem
Mirjalili, Seyedali
Rupapara, Vaibhav
Nappi, Michele
Griffith University Author(s)
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2021
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Abstract

Cardiovascular disease is a substantial cause of mortality and morbidity in the world. In clinical data analytics, it is a great challenge to predict heart disease survivor. Data mining transforms huge amounts of raw data generated by the health industry into useful information that can help in making informed decisions. Various studies proved that significant features play a key role in improving performance of machine learning models. This study analyzes the heart failure survivors from the dataset of 299 patients admitted in hospital. The aim is to find significant features and effective data mining techniques that can boost the accuracy of cardiovascular patient’s survivor prediction. To predict patient’s survival, this study employs nine classification models: Decision Tree (DT), Adaptive boosting classifier (AdaBoost), Logistic Regression (LR), Stochastic Gradient classifier (SGD), Random Forest (RF), Gradient Boosting classifier (GBM), Extra Tree Classifier (ETC), Gaussian Naive Bayes classifier (G-NB) and Support Vector Machine (SVM). The imbalance class problem is handled by Synthetic Minority Oversampling Technique (SMOTE). Furthermore, machine learning models are trained on the highest ranked features selected by RF. The results are compared with those provided by machine learning algorithms using full set of features. Experimental results demonstrate that ETC outperforms other models and achieves 0.9262 accuracy value with SMOTE in prediction of heart patient’s survival.

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IEEE Access

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9

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© The Author(s) 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Engineering

Information and computing sciences

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Engineering, Electrical & Electronic

Telecommunications

Information Systems

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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, 2021, 9, pp. 39707-39716

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