Design of an optimized rotation-invariant coordinate convolutional neural network driven medical IoT recommendation system integrating sentiment analysis for improved patient preference prediction

Abstract

Chronic and lifestyle-related diseases are rising globally, creating significant societal and economic burdens. To support effective long-term patient monitoring, an Optimized Rotation-Invariant Coordinate Convolutional Neural Network-driven Medical IoT Recommendation System integrating Sentiment Analysis for Improved Patient Preference Prediction (RICNN-IoT-SA-IPP) is proposed. The system collects multimodal data, including physiological and behavioural signals from IoT-based healthcare sensors and combines it with patient feedback sourced from electronic health records and medical consultation platforms. A Fast Guided Median Filter (FGMF) is employed to denoise and normalize the input, followed by spatial feature extraction utilizing Synchro-Transient-Extracting Transform (STET). These features are analyzed through a Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDSGAN) to infer patient sentiment. A Rotation-Invariant Coordinate Convolutional Neural Network (RICNN) then performs preference prediction. To enhance prediction accuracy, the Levy Pelican Optimization Algorithm (LPOA) is used for optimizing feature weights and model parameters. The system performance is evaluated using Accuracy, Precision, Recall, F1-Score, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Computational Time. The proposed RICNN-IoT-SA-IPP model achieved 99.32% accuracy and 98.34% precision, while maintaining low error rates with MAE = 0.0855 and MSE = 0.0864, respectively. When compared with existing models, these outcomes represent an improvement of approximately 3–5% in classification metrics and a significant reduction in prediction error. This demonstrates that the proposed framework provides highly accurate, reliable, and computationally efficient patient preference predictions.