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@article{204682,
        author = {Rajeev Ranjan},
        title = {DESIGN AND DEVELOPMENT OF INTERPRETABLE MACHINE LEARNING MODELS FOR HEALTHCARE PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {3513-3521},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204682},
        abstract = {The ever-increasing volume of healthcare data and the development of smart technology to aid in early assessment and good professional decision-making. Machine learning's (ML) capacity to sift through complicated medical information has made it an invaluable tool for healthcare forecasting. Results from many recent ML models are typically unclear, despite their excellence in prediction. This complicates their use, as trust and understanding are fundamental in practical therapeutic settings. The main objective of this project is to create an XML model for predictive healthcare with the goal of making medical decision support systems more accurate and easier to comprehend. The main goal of this effort is to construct a strong machine learning system that can accurately forecast serious health issues and provide thorough explanations for such predictions. Advanced ensemble learning methods are used in the study to enhance the prediction accuracy. The study also looks at common problems with healthcare datasets such uneven distribution of classes, data loss, and data unpredictability. The proposed framework makes use of Explainable Artificial Intelligence (XAI) techniques to ensure that it is easy to understand. Methods like feature value analysis and model explanation tools fall within this category. We evaluate the suggested model using conventional performance measures including accuracy, precision, recall, F1-score, and AUC-ROC to make sure it works and is reliable. The results show that the model's capacity for future prediction and patient comprehension is enhanced when explainability is included in ensemble learning. This makes it more applicable to real-life healthcare environments. By connecting theoretically solid prediction models with their practical use in healthcare, this study adds to the growing field of AI-driven healthcare. Improved decision-making speed, accuracy, and clarity might be achieved by healthcare practitioners using the explainable ML model that was built. More trust in AI-driven healthcare systems and better health outcomes for patients should be the final product.},
        keywords = {},
        month = {June},
        }