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.
Multiresolution classification with semi-supervised learning for indirect bridge structural health monitoring
