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Machine Learning in Health Care

Machine learning (ML) is predominately being used to solve various technical and nontechnical challenges around the world. Taking the context of health care, ML took over the control and helping many practitioners for effective decision-making. Practically, this is once again proved by the researchers as they are using the ML algorithms for fast detection of COVID-19 and steps are initiated for the drug discovery of the same. In this chapter, we initially discuss the work happening in COVID-19 using ML algorithms. Then, we summarize the role of ML for analyzing and assessing various chronic diseases. Even though ML algorithms are predicting multidimensional aspects of the target disease, the experts in the field are still hesitating to use those outcomes as they lack in justification. To address this, a separate concept called explainable AI (XAI) is discussed. Data scientists are using ML algorithms to address chronic health issues with less cost and in a more accurate way. The question is, how ML could achieve this? This will be the main motivation for the entire chapter. In a recent report of a Harvard Medical Survey, it said that nearly 5, 000, 000 Indians die every year due to the medical errors (Harvard, 2020). These errors are getting reduced when the same disease is analyzed with ML. There is a famous proverb, “Prevention is better than cure.” According to World Health Organization, in most of the cases, chronic diseases cannot be prevented. But, the impact of them can be reduced with proper data collection and effective analysis using advanced ML algorithms. For instance, to detect lung cancer, it would cost hundreds of dollars for undergoing diagnosis and medication. Above that, if the disease is detected in the advanced stages then the probability of the cure is also very less. All these negative consequences can be reduced drastically when the usage of ML for health care is further exploited. © The Institution of Engineering and Technology 2021.