Abstract
It would, therefore, require highly advanced prediction tools to enhance early diagnosis and preemptive mechanisms for all these burgeoning diseases. Fast and correct disease prediction and pre-emption have huge potential for changing clinical outcome and ensuring timely and effective interventions that reduce morbidity and mortality. Current predictive models, instrumental as they are, have been found faltering in precision, recall, accuracy, and timeliness. Such delays and inaccuracies often miss the therapeutic window or lead to misguided clinical decisions. In this work, we present a novel model that aims to quite dramatically improve the process of segmentation and classification. Our approach embeds Attention Mechanisms with Adversarial Training and Ensemble Deep Learning Operations, together with a multimodal approach, which places it substantially higher across several metrics. This improves the precision, accuracy, recall, and AUC by 8.5%, 8.3%, 4.9%, and 3.9%, respectively, for segmentation and classification, while reducing the classification delay by 5.9% in different situations. Not only does our model handle the intrinsic limitations of current methods, but it also shows flexibility for a wide range of clinical applications. The compelling improvements in classification and preemption metrics strengthen its potential to make a sea change in the disease prediction framework for establishing optimum patient outcomes and efficient scenarios of healthcare delivery.