Bayesian Models for Healthcare Data Analysis

The rapid increasing amount of healthcare data poses great challenges to data mining and machine learning study and applications. Recently a large number of algorithms and models have been proposed to discover knowledge and information from large scale healthcare datasets. In medical applications, confidence measured by posterior probability is well accepted since it can quantify the certainty or severity of targets. In this article, we propose a sparse Bayesian model for healthcare data analysis. The proposed model utilizes a set of basic functions and it learns a sparse weight vector to combine them together. Our model is a fully Bayesian method which can incoporate a prior and derive a likelihood function from a given training data set. Working with the images of Pulmonary Embolism diagnosis dataset and Breast Cancer clinical dataset from KDDCup, our experiments demonstrate that the Bayesian approach lead to 83% and 80% test accuracy in modeling principles of healthcare data and it significantly improves the performance of its couterparts.

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