A machine learning approach for risk factors analysis and survival prediction of Heart Failure patients

Md Mamun Ali, Vian S. Al-Doori, Nubogh Mirzah, Asifa Afsari Hemu, Imran Mahmud, Sami Azam, Kusay Faisal Al-tabatabaie, Kawsar Ahmed, Francis M. Bui, Mohammad Ali Moni

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)
    80 Downloads (Pure)

    Abstract

    In this study, we propose machine learning (ML) for risk factors analysis and survival prediction of Heart Failure (HF) patients using a survival dataset. Five supervised ML methods are applied to the dataset: Decision Tree (DT), Decision Tree Regressor (DTR), Random Forest (RF), XGBoost, and Gradient Boosting (GB) algorithms. We compare the applied algorithms’ performances based on accuracy, precision, recall, F-measure, and log loss value and show RF provides the highest accuracy of 97.78%. The analysis of the risk factors shows the most predictive features based on coefficients and feature importance. The top six risk factors for HF patients are serum creatinine (SC), age, ejection fraction (EF), platelets, creatinine phosphokinase (CPK), and SS (SS). Further analysis of these factors shows significant clustering of the features. The survival analysis finds that the increment of SC, age, and SS and the decrement of EF are the most significant risk factors for HF patients. Our results suggest that HF survival prediction is possible with higher accuracy using the proposed model. Our ML models are useful in clinical settings for screening patients with HF probability.

    Original languageEnglish
    Article number100182
    Pages (from-to)1-12
    Number of pages12
    JournalHealthcare Analytics
    Volume3
    DOIs
    Publication statusPublished - Nov 2023

    Bibliographical note

    Funding Information:
    This work was supported in part by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) .

    Publisher Copyright:
    © 2023 The Author(s)

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