Extended Early Disease Predicting Futures with Advanced Mutation Detection Analysis and Modeling by Using DNA Sequencing Techniques

Viruses and bacteria are continuously evolving and spreading rapidly from one individual to another. In recent years, especially during the COVID-19 pandemic, we witnessed how severely the coronavirus affected the human population. Identifying such diseases has become increasingly challenging due to limited knowledge and rapid mutation of pathogens. DNA plays a crucial role in understanding how a living organism is affected by a specific virus, providing detailed insights into both the host and the pathogen. This proposed paper explores various approaches for predicting human diseases using DNA sequencing technologies. It discusses the applications, methodologies, and challenges involved in disease prediction through DNA sequencing. The paper also presents a comprehensive review of advancements in DNA sequencing techniques. A solution is proposed in which DNA samples collected from the National Centre for Biotechnology Information (NCBI) are processed and classified into disease patterns. These patterns are then compared with sequences from newly affected individuals to identify potential diseases. For classification, machine learning-based models such as Support Vector Machine (SVM), Naive Bayes, Random Forest, Decision Tree, and XGBoost are utilized. In addition, deep learning-based models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are employed for enhanced classification accuracy. Among these, the proposed Random Forest model achieves the highest accuracy of 97.2%, showing an improvement of 2.90% over existing models.