Machine learning parameter estimation in a smart-city paradigm for the medical field

Abstract

Machine learning (ML)-based parameter estimation and classification have been receiving great attention in data modelling and processing. The Gaussian mixture model (GMM) is a probabilistic model that represents the presence of subpopulations, which works well with a parameter estimation strategy. In this chapter, maximum likelihood estimation based on expectation maximization is used for the parameter estimation approach; the estimated parameters are used for the training and testing of medical images for normality and abnormality. The mean and the covariance, considered to be the parameters, are used in GMM-based training for the classifier. Support vector machine (SVM), a discriminative classifier, and the GMM, a generative model classifier, are the two most popular techniques. The classification strategy performances of both classifiers have better proficiency than other classifiers. By combining the SVM and GMM, it is possible to improve classification because estimating the parameters through GMM has very limited features; hence, there is no need to use any feature reduction techniques. The features extracted were used for the training of the classifiers. The testing of medical images for normality was performed with respect to the features that were trained.