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@article{186013,
        author = {SREEVIDYA.N.R. and Dr. L. Sudha},
        title = {Revolutionizing Smart Healthcare: Efficient Neural Network Synthesis for Enhanced Patient Outcomes},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {3629-3633},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186013},
        abstract = {In the rapidly evolving domain of smart healthcare, technology plays a pivotal role in transforming patient care and outcomes. Leveraging advanced technologies, smart healthcare introduces a paradigm shift from traditional methods to innovative, data-driven solutions. At the heart of this transformation lies the integration of artificial intelligence (AI), particularly neural networks, which have demonstrated significant potential in diagnosing, monitoring, and treating various medical conditions. Neural networks, inspired by the human brain, are a subset of AI that can identify patterns, learn from data, and make decisions with minimal human intervention. Their application in healthcare is multifaceted, ranging from image analysis in radiology to predicting patient outcomes based on historical data. As we delve deeper into smart healthcare, understanding how these neural networks are synthesized and their subsequent impact on patient care becomes crucial. The synthesis of neural networks involves the process of designing and training these models to perform specific tasks effectively. This process is critical in ensuring that the networks operate efficiently and accurately, particularly in the high-stakes environment of healthcare. By optimizing neural network synthesis, we can enhance their performance, ultimately leading to better patient outcomes and more streamlined healthcare processes.},
        keywords = {Healthcare, Big Data, Neural Network},
        month = {October},
        }