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

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    Author(s)
    Ishaq, Abid
    Sadiq, Saima
    Umer, Muhammad
    Ullah, Saleem
    Mirjalili, Seyedali
    Rupapara, Vaibhav
    Nappi, Michele
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    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 ...
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    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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    Journal Title
    IEEE Access
    Volume
    9
    DOI
    https://doi.org/10.1109/ACCESS.2021.3064084
    Copyright Statement
    © 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.
    Subject
    Engineering
    Science & Technology
    Engineering, Electrical & Electronic
    Telecommunications
    Information Systems
    Publication URI
    http://hdl.handle.net/10072/403697
    Collection
    • Journal articles

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