ALGORITHMS OF ML TO UNDERSTAND BIOLOGICAL BIG DATA IN PERSONALIZED MEDICINE

Abstract

Big Data technologies, among other fields of use, have the potential to alter healthcare, global health, and clinical research. In the healthcare sector, big data sources include the internet of things, medical notes, patient information, medical screening tests. Data must be carefully stored and evaluated in addition to providing useful information. Consequently, healthcare professionals must be completely provided with the appropriate framework to produce and study big data in addition to providing effective strategies for improving health equity. Big Data which is well organized, analyzed, and evaluated has the potential to improve the future by providing more possibilities for healthcare. With a robust combination of biological and healthcare data, current health organizations might perhaps transform medical treatments and personalized medicine. The scope of the thesis is to highlight the need of big data analytics in healthcare, explain data processing pipeline, and algorithms of machine learning used to analyzed big data.