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@article{206727,
        author = {PaulDavidson Koilpandi},
        title = {Intelligent Disease Prediction Using Deep Learning: A Biomedical Engineering Approach"},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2607-2609},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206727},
        abstract = {The early and accurate prediction of disease remains a critical challenge in modern healthcare, particularly as clinical data grows in volume and complexity. This paper proposes an intelligent disease prediction framework that leverages deep learning architectures, namely Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to classify patient health status from clinical and physiological data. The proposed system was evaluated against conventional machine learning baselines including Logistic Regression, Random Forest, and Support Vector Machine. Experimental results indicate that the proposed CNN-based model achieved the highest classification accuracy of 94.7%, outperforming traditional approaches by a significant margin. The findings demonstrate the potential of deep learning to support clinical decision-making and enable early diagnosis in biomedical engineering applications. This paper discusses dataset characteristics, preprocessing strategies, model architecture, and comparative performance analysis, providing a reproducible framework for future research in AI-driven healthcare systems.},
        keywords = {Deep Learning, Disease Prediction, Biomedical Engineering, Convolutional Neural Network, LSTM, Healthcare Informatics, Artificial Intelligence},
        month = {July},
        }