ABSTRACT
Currently, machine learning and blockchain play a predominant role in the health care sector. In health care, machine learning is most commonly used for administrative purposes, clinical decision support, diagnosis and treatment suggestions, and administrative tasks, such as automating medical bills, improving and managing health records, and others. Whereas blockchain technology is employed to ensure forge-free management of the drug supply chain, safeguarding the health record of a patient, enhancing security in order to protect medical services like insurance, and so on, machine learning allows for the analysis of thousands of different data points and the prediction of results, as well as the provision of timely risk ratings and accurate resource allocation. This chapter discusses and analyzes the implementation of different machine learning approaches, such as the decision tree algorithm, the random forest algorithm, the support vector machine algorithm, linear regression, deep learning models, and others, in medical applications such as glaucoma diagnosis, heart disease detection in diabetic patients, the detection of dementia, breast cancer detection, and the application of blockchain in for maintaining and securing the medical records of patients, avoiding scams in medical insurance, and managing the drug supply chain. These developing technologies act as powerful tools for clinicians for improving the health care systems.
