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@article{204684,
        author = {Rajeev Ranjan},
        title = {An Explainable Machine Learning Approach to Personalized Healthcare Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {3600-3611},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204684},
        abstract = {A new paradigm in contemporary medicine, personalised healthcare seeks to tailor diagnosis, treatment, and preventative measures to each patient by analysing their unique set of medical history and other personal factors. While healthcare organisations have benefited greatly from Machine Learning (ML) integration in terms of improved predictive skills, many ML models' complexity makes them difficult to understand and use. This research presents a method for personalised healthcare prediction using Explainable Machine Learning (XML) that integrates both high predictive accuracy and model interpretability. Health outcomes and illness risks may be predicted using the framework by analysing patient demographics, clinical data, test findings, lifestyle variables, and medical history. For prediction, we use state-of-the-art ML algorithms. To understand how the model makes its decisions, we incorporate explainability techniques like SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and feature importance analysis. Improved trust, responsibility, and clinical decision support are outcomes of the suggested method, which helps healthcare providers comprehend the elements impacting forecasts. The explainable model maintains openness and user interpretability while achieving dependable prediction performance, according to experimental assessment. These results demonstrate the promise of explainable AI for enhancing medical decision-making, patient outcomes, and personalised healthcare methods.},
        keywords = {Health Care Forecasting, XML-Based Explainable Machine Learning, Predictive Analytics, XAI-Based Healthcare Prediction, AI Models with Full Transparency},
        month = {June},
        }