Big data in oncology: Extracting knowledge from machine learning

Large amounts of data, rising healthcare expenses, and a focus on individualized treatment have all conspired to push the use of big data in healthcare to new heights in the last few years. Data that is too large or complicated for traditional data processing technologies to decipher is referred to as "big data" in the healthcare industry. Electronic medical records and electronic health records (EMR/EHR) contain data on patient history, diagnosis and treatment, medications, treatment plans, allergies, research center and test results, genomic sequencing, medical imaging, health insurers, and other clinical information. The Internet of Things (IoT) and EMR/EHRs are two examples of big data sources for healthcare. A variety of machine learning algorithms were tested on a variety of healthcare datasets and the results are presented in this study. Big data processing, management, and application issues are also discussed. For this article, machine learning techniques and the necessity to deal with and use large data from a new viewpoint will be discussed.