International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-13 Issue-139 June-2026
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An integrated machine learning and survival analysis framework for heart failure classification and prognosis

Islam D. S. Aabdalla1 and D. Vasumathi1

Department of Computer Science and Engineering,Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad, Telangana,India1
Corresponding Author : Islam D. S. Aabdalla

Recieved : 08-October-2024; Revised : 15-May-2026; Accepted : 19-May-2026

Abstract

Heart failure (HF) significantly contributes to global morbidity and mortality, highlighting the need for strategies for early diagnosis and prognosis. This study investigates the application of machine learning (ML) techniques to improve the accuracy of HF diagnosis and survival analysis (SA). A comprehensive framework was developed by integrating four feature selection methods Kruskal-Wallis, analysis of variance (ANOVA), Relief, and Chi-squared (χ²) to identify the most effective clinical features for classification. Among the evaluated classifiers, the light gradient boosting machine (LightGBM) with noise augmentation achieved the highest classification accuracy of 99.98%. Concurrently, the random survival forest (RSF) model achieved a strong concordance index (C-index) of 0.85, demonstrating its effectiveness in survival prediction. The survival modelling framework further incorporated deep survival (DeepSurv), RSF, and extreme gradient boosting (XGBoost). In addition, the Cox proportional hazards (CPH) model was employed to enable robust prognosis prediction, while dataset augmentation techniques such as synthetic minority over-sampling technique (SMOTE)-based mixup, noise injection, and three-level augmentation were used to enhance model generalisability. The performance was evaluated using several ML models, including random forest (RF), k-nearest neighbours (KNN), gradient boosting machine (GBM), XGBoost, decision trees (DT), and logistic regression (LR). Comparative analysis with existing studies demonstrated the superior accuracy and detection capability of the proposed approach. The findings confirm the significant potential of ML techniques in improving HF patient management and predicting long-term clinical outcomes.

Keywords

Heart failure diagnosis, Machine learning, Survival analysis, Random survival forest, LightGBM, Clinical prognosis.

Cite this article

Aabdalla IDS, Vasumathi D. An integrated machine learning and survival analysis framework for heart failure classification and prognosis. International Journal of Advanced Technology and Engineering Exploration. 2026;13(139):968-987. DOI : 10.19101/IJATEE.2024.111101839

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