Title: Machine Learning Techniques for Medical Analysis.
Abstract: Recent research in machine learning has shown that it incorporates a small amount of dependencies among attributes involved in a database and represents them graphically in the form of a network, known as Tree Augmented Naive Bayes (TAN). This is competitive with the state-of-the-art classifiers such as the C4.5 decision tree and naive Bayes classifier. This thesis explores approaches for constructing Bayesian networks, based on the theory of naive Bayes classifier and existing dependencies among attributes.One of the proposed approaches for finding the dependencies, is to apply naive Bayes classifier between the attributes itself, then using the attributes with dependencies to construct Bayesian network. This proposed approach is called Bayesian Network with Naive Dependence (BNND). The prediction performance from this network is competitively better than the C4.5 decision tree and naive Bayes classifier. However, if there are too many dependencies found, then the prediction performance of the network is significantly reduced. Therefore, this paper also explores the uses of feature selection to select well-related attributes, in order to reduce the chance of over-fitting in Bayesian network and to increase the computational time. These Bayesian networks are experimented using medical databases from the machine learning repository in the University of California, Irvine. The results are compared with those from a C4.5 decision tree, naive Bayes classifier and TAN in terms of classification performance, sensitivity, specificity and computational time. 
For More About This Project Details Click On Download As medical information systems in modern hospitals and medical institutions become larger and larger, it causes great difficulties in extracting useful information for decision support, especially when traditional manual data analysis has become inefficient and methods for efficient computer-based analysis are indispensable. Therefore, it is time to introduce a modern, efficient and effective computer-based method in medical analysis for decision support. There are many different forms of medical data analysis: statistical, machine learning and data abstraction. Lavrac in 2000 stated that “Machine learning is a substantially more mature technology than data abstraction”, for this reason, the new computer-base method is surrounded by machine learning techniques. Medical analysis using machine learning techniques has been implemented for the last two decades.
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