Logistic Regression-Based Machine Learning Model for Mutation Classification in the Discovery of Precision Medicine

Abstract

Mutating medicine takes a vital role in precision medicine. Precision medicine enables customization and personalization of healthcare technology. Advancements in machine learning, deep learning and soft computing-based intelligent system techniques can support and improve the process in precision medicine. The machine learning-based supervised learning algorithms take training data and perform classification or prediction according to the application. It supports precision medicine and mutating medicine with improved accuracy and performance in prescribing the medicine. The proposed method uses logistic regression-based machine learning model for mutation classification in order to discover precision medicine. Logistic regression is the well-known machine learning-based statistical model for classification. The logistic regression-based method proves its efficiency by comparing it with other state-of-the-art machine learning and deep learning classification models. Based on the obtained result the proposed LRBMM model performs well in mutation classification. Applying machine learning techniques made significant improvements in precision medicine.