Darya Forouzanfar
ABSTRACT
In this paper, we present a finite mixture model with a generalization of Beta distribution called the McDonald's Beta Mixture Model (McBMM). The parameters of the McBMM are estimated via the maximum likelihood estimation technique and EM algorithm using the Newton-Raphson technique as an iterative approach that assists in computing the updated parameters. We apply our proposed model to medical applications namely, targeting treatment for heart disease patients based on clinical data, breast tissue analysis considering histopathological images, and malaria detection using histological images. Compared to the Gaussian mixture model (GMM), the Beta mixture model performs better on data with strictly bounded values and asymmetric distribution. Three real-world datasets are modelled using the new McBMM, showing that this model fits better than the GMM and has better accuracy.
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Index Terms
- Finite Multivariate McDonald's Beta Mixture Model Learning Approach in Medical Applications
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