The rapid advancement in medical technologies has revolutionized the field of predictive healthcare, enabling the development of sophisticated systems that integrate diverse data types to enhance diagnostic accuracy. This paper presents a novel hybrid medical prediction system that synergistically combines deep learning-based image analysis and traditional medical data processing techniques to deliver accurate multi-modal diagnostic predictions.The proposed system leverages the strengths of Convolutional Neural Networks (CNNs) and Natural Language Processing (NLP) techniques to synthesize insights from medical images, structured data, and textual information. Specifically, ResNet-18 is employed for feature extraction from medical images, while term frequency-inverse document frequency (TF-IDF) vectorization is utilized for processing structured and textual data.By integrating CNNs with NLP techniques, the system forms a robust architecture capable of identifying complex relationships between diverse data modalities. This multi-modal approach not only enhances diagnostic accuracy but also streamlines clinical workflows, offering significant potential in predictive healthcare systems.The proposed hybrid medical prediction system has far-reaching implications for the field of healthcare, enabling clinicians to make more informed decisions, improving patient outcomes, and reducing healthcare costs. Future research directions include exploring the application of this system to various medical specialties and investigating the use of other deep learning architectures to further enhance diagnostic accuracy.
Natural Language Processing (NLP), CNN, Image classification, Text Vectorization
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