Abstract
The era of intelligent algorithms has arrived, and machine learning is one of the most promising technologies to revolutionize healthcare. Until recently, manufacturing, transportation, and administration were the primary industries where machine learning algorithms had a significant impact. However, even formerly impervious industries like healthcare are suddenly being affected by these algorithms. While machine learning has been around for quite some time, its use in healthcare is continuously increasing alongside the availability of data. It is a statistical method that allows computers to learn from past data. They are able to identify patterns and come to conclusions or judgments depending on the information that they are presented with. Machine learning (ML) has numerous prospective applications within the healthcare industry. They extend from drug discovery to clinical decision-making and diagnosis. There are petabytes of healthcare-related data that require analysis. For instance, the human genome is an example of this, which is approximately 100 gigabytes per person. Furthermore, carry-and-wear devices generate a large quantity of data, including heart rate, blood pressure, and walking pattern. Therefore, on the basis of these data, ML techniques can be used to predict diseases and develop personalized treatments. Moreover, X-ray and MRI image classification techniques can be used to construct an ML algorithm for potential disease diagnosing, thereby reducing the burden on clinicians. Likewise, in drug discovery and development, ML algorithms have been utilized to help identify novel therapeutic targets, design new drug candidates, and predict drug toxicity. ML techniques can be used to create predictive models for patient outcomes like mortality, readmission, and disease progression. ML algorithms can be put to use to analyze electronic health record (EHR) data to facilitate clinical decision-making, such as predicting patient readmission rates or identifying patients who may benefit from a specific treatment. Therefore, ML has the potential to revolutionize the healthcare industry by providing methods to cluster, classify, predict, and assist clinicians in making informed decisions. Consequently, this chapter will investigate the current state of machine learning (ML) in the healthcare industry, as well as the challenges it faces and its future development potential.
