Predictive Modeling and Analysis in Genetic Diseases: A Comprehensive Review of Recent Advances

Abstract

This paper provides a comprehensive analysis of current advancements in predictive modeling and genetic disease classification. We delve into various machine learning techniques and text mining technologies that have significantly contributed to understanding genetic disorders and extracting valuable information from vast unstructured data. Our literature review examines key studies from recent years that have utilized machine learning models, including Naive Bayes, support vector machines, and deep learning frameworks, to improve the predictive accuracy of genetic disease outcomes. This work is aimed at enhancing the framework for predicting complex diseases using advanced computational methods.