Abstract
The proactive anticipation of disease occurrence stands as a pivotal facet within healthcare and medical research endeavors, dedicated to forecasting the probability of an individual manifesting a particular medical condition or ailment in the future. This fundamental pursuit integrates diverse data reservoirs, encompassing medical history, genetic profiles, lifestyle determinants, and emerging technological advancements, to construct predictive frameworks capable of furnishing early indications and insights pertaining to potential health vulnerabilities. The overarching aim of disease prediction resides in furnishing healthcare practitioners and individuals alike with the requisite knowledge and resources to undertake pre-emptive measures, render informed choices, and ultimately enhance holistic health and well-being. The Neural Network algorithm emerges as a dependable approach for disease prognostication, offering heightened precision and several advantages compared to the conventional methodologies, including its capacity to discern intricate features from images and its adaptability across diverse computing platforms. The proposed study offers a comprehensive review of disease prediction methods, comparing conventional approaches with machine learning interventions to provide swift and reliable results. Further review suggests a proposed model that utilizes the neural network algorithms to overcome the shortcomings of conventional methods.
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