Machine Learning in the Healthcare Sector and the Biomedical Big Data
Tanvir Habib Sardar, Amina Khatun, Souvik Sengupta, Yusuf Alam, Tabassum Ara
Abstract
Global population rise is accompanied by an increase in illnesses among people. Doctors are unable to keep up with the rapid increase of new diseases being created. Unlike the rate at which the population is growing, the number of doctors is not growing at the same rate. Because of a lack of time and appropriate examination, many patients are passing away too soon or facing trouble. In the near future, the society will have a particular need for competent doctors, prompt disease diagnosis, prompt medication delivery, and precise diagnosis. Due to a shortage of highly skilled, experienced, and saintly doctors and an inadequate examination of the ailment, patients squander a lot of money and time. Machine learning (ML) applications are becoming indispensable for resolving these issues. It is important to provide the machine with adequate and relevant training data provided by the doctor or healthcare system so that it can quickly and accurately identify the pattern or an etiology of a disease. The incorporation of ML techniques in conventional diagnosis is essential for the vast data set size in healthcare and the significant value that precise predictions contain. This chapter seeks to serve as both a compilation and an evaluation of the several cutting-edge ML algorithms that are currently being integrated with human interaction machines. This chapter provides an overview of the numerous healthcare application domains that use these ML techniques from a security and privacy perspective as well as any associated difficulties in healthcare applications. Finally, we provide light on the difficulties facing the field of research today and propose promising avenues for research.