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@article{209952,
        author = {KAVIYA R and ANANTHI B},
        title = {Fairness-Aware and Explainable Machine Learning for Healthcare Prediction: A Systematic Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {3921-3925},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209952},
        abstract = {Machine learning models trained on structured health data, such as electronic health records, insurance claims, registries and population surveys, are increasingly used for risk prediction and resource allocation. When the data or the problem formulation reflects existing inequities, these models can reproduce or amplify them, so that some patient groups receive less accurate predictions or less access to care. This paper reviews methods for mitigating bias in such models. We organise the literature by the stage at which mitigation is applied (pre-processing, in-processing and post-processing), summarise the fairness metrics used to judge success, and compare prior reviews and empirical studies. The evidence shows that pre-processing methods such as reweighing dominate current practice and can reduce measured disparities, that removing the protected attribute is often insufficient, that fairness constraints can reduce performance, and that no single model or metric is best. Gaps include inconsistent reporting, limited external validation and intersectional analysis, and weak links between fairness metrics and health outcomes. We conclude with practical recommendations for developing and reporting fairer clinical prediction models.},
        keywords = {Algorithmic Bias, Fairness, Bias Mitigation, Structured Health Data, Electronic Health Records, Machine Learning, Health Equity.},
        month = {October},
        }