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Healthcare is one of the important real-life applications of machine learning. Machine learning has delivered very powerful automated systems to the humanity such as automated vehicles and voice-enabled personal assistant system. Now in the health domain, digitization of patient data produces a lot of information. This information is used to analyze the health records and predict the future health conditions of the patients. In this chapter, we elaborate various algorithms of machine learning. We analyze the basic understanding of the concept of these algorithms in healthcare domain and working fashion. We discuss the current trends of machine learning in identifying and diagnosing diseases using medical imaging techniques, drug discovery, robotic surgery, and smart fitness equipment. We have elaborated many ML algorithms which are already working in this domain like SVM, logistic regression, random forest, linear regression, etc. In this study, we also talk about the various challenges for the medical care industry and threats. And as the conclusion, we find that algorithms are being optimized for better performance and the selection of an optimal algorithm is a challenging task in AI. And engineers of AI devices should be aware of the possible unintended consequences of their methods and guarantee that the calculations are done in view of the worldwide network. |
