Predictive Analytics in Healthcare Using Machine Learning Tools and Techniques

Healthcare is a term that relates to a system that involves advanced strategies to serve the medical demands of the people. Healthcare organizations of all sizes, types, and specialties are becoming increasingly interested to adopt a mechanism that promotes reduced costs and improving efficiencies. The healthcare industry faces challenges in the essential areas like electronic record management, data integration, and computer-aided diagnoses and disease predictions. From past years, healthcare research with machine learning (ML) has been increasing steadily. Due to variety of medical data including clinical data, omics data, or electronic health records (EHR) data, it is difficult for humans to infer the data and to make decisions. ML uses various statistical techniques and advanced algorithms to predict the results of healthcare data more precisely. ML is a subarea of artificial intelligence that has mechanism of making machines learn automatically without being explicitly programmed. Data being the important component of ML, when presented to these systems, they learn, change, grow, and implement by themselves. Three categories of classification, supervised, unsupervised, and reinforcement learning, are used for analysis by ML. This chapter depicts the study of various prediction techniques and tools in practice. A comparative study on techniques and tools will be carried out. A glimpse on scopes and challenges using ML will also be discussed. Applications of ML in healthcare will also be illustrated in the chapter. This chapter examines the study of various prediction techniques and tools in practice. It discusses a glimpse on scopes and challenges using machine learning (ML), and illustrates the applications of ML in healthcare. ML, an advanced predictive analytics algorithm, has been proved to achieve the goals and is widely used by the healthcare industry nowadays. The approaches to predictive analytics include regression analytics and ML analytics. ML has many algorithms for data classification and a prediction that helps to achieve speed, accuracy, and medical staff for quality treatment. Tools play an important role in ML. Selecting the correct tool is as necessary as applying the best algorithm for the purpose. One of the main tasks in designing any model is evaluating the performance. There are various metrics to test the performance of the ML algorithm. Cost function and lost function serve the purpose of estimating the performance of the algor