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@article{174288,
        author = {Vibinsuryaa M and Solomon A and Praveenkumar A K and Srikrishnaprasanth N and Dr. A. S. Muthanantha Murugavel},
        title = {DATA EXPLORATION AND DEMAND PREDICTION IN MEDICAL SUPPLY: A MACHINE LEARNING APPROACH},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4117-4121},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174288},
        abstract = {The healthcare industry relies heavily on the well-organized management of medical supplies to ensure timely and quality patient care. Accurate demand prediction for medical supplies is critical to prevent shortages, optimize stock levels, and reduce wastage. This study explores data-driven approaches to medical supply management by conducting an in-depth analysis of historical demand data and smearing machine learning techniques to foresee future demand. The data exploration phase involves cleaning and preprocessing data from various sources, shadowed by analyzing trends, seasonality, and anomalies. Insights gained from data exploration help identify key factors influencing demand, such as seasonal patterns, economic conditions, and population health metrics. The study creates and tests various machine learning models for demand prediction, such as time series models, regression techniques, and advanced deep learning methods. Prediction accuracy and the capacity to identify intricate correlations in the data are the two main criteria used to evaluate models. To identify the best-fit perfect for medicinal supply-demand forecasting, the study evaluates the effectiveness of more modern machine learning techniques against more conventional statistical methods.},
        keywords = {Demand Prediction, Seasonality Analysis, Supply Chain Optimization.},
        month = {July},
        }