Machine Learning for Health Data Analytics: A Few Case Studies of Application of Regression

Machine Learning for Health Data Analytics: A Few Case Studies of Application of Regression

Muralikrishna Iyyanki (Independent Researcher, India) and Prisilla Jayanthi (Administrative Staff College of India, India)
DOI: 10.4018/978-1-7998-0182-5.ch010

Abstract

At present, public health and population health are the key areas of major concern, and the current study highlights the significant challenges through a few case studies of application of machine learning for health data with focus on regression. Four types of machine learning methods found to be significant are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. In light of the case studies reported as part of the literature survey and specific exercises carried out for this chapter, it is possible to say that machine learning provides new opportunities for automatic learning in expressive models. Regression models including multiple and multivariate regression are suitable for modeling air pollution and heart disease prediction. The applicability of STATA and R packages for multiple linear regression and predictive modelling for crude birth rate and crude mortality rate is well established in the study as carried out using the data from data.gov.in. Decision tree as a class of very powerful machine learning models is applied for brain tumors. In simple terms, machine learning and data mining techniques go hand-in-hand for prediction, data modelling, and decision making. The health analytics and unpredictable growth of health databases require integration of the conventional data analysis to be paired with methods for efficient computer-assisted analysis. In the second case study, confidence interval is evaluated. Here, the statistical parameter CI is used to indicate the true range of the mean of the crude birth rate and crude mortality rate computed from the observed data.
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2. Machine Learning [Ml] And Data Mining [Dm]

ML uses human-based algorithm and works everything without the use of humans’ interference; once implemented, the outcome is accurate because the process is automated. Also, ML is capable to take the own decision and resolve the issue. Ever growing ML techniques, overwhelms problems associated with DM techniques as ML techniques are more accurate and less error prone compared to DM. This self-learning technique is not available in DM whereas ML uses self-learning algorithms to improve its performance as an intelligent task with experience over time (Brooks & Dahlke 2017). To summarize the foregoing, it can be stated the following are the definitions and differences or commonalities, if any, between ML and DM:

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