Hybrid Linguistic and Machine Learning Solutions in Healthcare

Abstract

Healthcare data presents substantial hurdles in extracting essential features that aid in clinical decision-making due to its rapid growth. The proposed work addresses the issues of increasing the efficacy and accuracy of medical data analysis and classification in the face of quickly changing healthcare regulations, high operating costs, and workforce constraints. Considering these limitations, we propose a hybrid linguistic machine learning system that combines natural language processing for feature extraction with a seagull-optimized deep neural model for classification. The system is developed with a user-friendly interface that allows for easy interaction with healthcare practitioners to make informed recommendations. The proposed system's uniqueness stems from the model's design and development, which mixes seagull-inspired optimization with deep neural architecture to improve classification outcomes. The suggested model is evaluated based on standard performance criteria such as accuracy, precision, recall, and F-score. It is compared to classic models such as support vector machine, decision tree, random forest, and flattened-feed forward neural network. The findings show that the suggested model surpasses the existing techniques, with 96.8% accuracy, 95.4% precision, 94.9% recall, and 95.1% F-score. These findings support the proposed system's ability to improve healthcare data processing, perhaps leading to better patient outcomes and more informed clinical decisions.

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