Big Data-Driven Intelligent Healthcare Analytics Platform for Predicting Patient Outcomes and Personalized Treatment Using Deep Recurrent Convolutional Neural Network

Healthcare is evolving through the integration of massive datasets and advanced artificial neural networks, enabling individualized treatment plans and accurate patient outcome forecasting. To address the challenges of managing and analyzing large, heterogeneous medical datasets, this research proposes a Big Data-Driven Smart Healthcare Analytics Infrastructure powered by Deep Recurrent Convolutional Neural Networks (DRCNNs). Key concerns include integrating multimodal information sources such as genetic profiles, data from smart devices and Electronic Health Records (EHRs) while ensuring data confidentiality and the scalability of evaluation frameworks. The proposed platform employs a hybrid DRCNN architecture that combines recurrent layers for capturing temporal patterns in human health trajectories with convolutional layers for feature extraction. To enhance forecasting accuracy and generalization, the system incorporates dynamic feature selection, iterative training, and advanced preprocessing techniques. The primary objectives are to improve patient outcome prediction models and generate personalized treatment recommendations based on patient-specific information. Experimental results demonstrate that the platform surpasses conventional models in prediction accuracy, responsiveness, and specificity, highlighting its potential to enhance clinical decision-making and medical outcomes. This study underscores the transformative role of big data and deep learning in developing patient-centered, intelligent medical systems that enable personalized and predictive therapies.