Predicting Disease Futures with Advanced mutation detection Analysis and Predictive Modeling by using DNA sequences

Viruses and bacteria are constantly evolving in the world and spreading very fast, jumping from one human body to another. Nowadays, to identify these diseases is very challenging due to a shortage of knowledge about viruses and bacteria. DNA is one of the keys through which we can know about a living body being affected by a particular virus as well as detailed information about that virus. This paper suggests approaches to predict human diseases using DNA sequencing technologies. Applications and challenges in the prediction of human diseases using DNA sequencing techniques and methodologies will also be discussed. The paper summarizes a methodological review of DNA sequencing technologies with its advancements. This paper has also presented a solution in which samples of DNA collected from NCBI (National Center for Biotechnology Information) are used for DNA classification after being converted into disease patterns. These patterns are then compared with newly affected persons to identify the diseases. Machine learning based classifiers, including SVM, Naive Bayes, Random Forest technique, Decision Tree, and XGBoost, are used in this research as well as deep learning-based classifiers CNN, RNN are used to classify the disease. Additionally, proposed method, which gives higher accuracy than other classifier methods, is presented.