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Abstract

Due to the significant amount of data generated by modern medicine there is a growing reliance on tools such as data mining and knowledge discovery to help make sense and comprehend such data. The success of this process requires collaboration and interaction between such methods and medical professionals. Therefore an important question is: How can we strengthen the relationship between two traditionally separate fields (technology and medicine) in order to work simultaneously towards enhancing knowledge in modern medicine. To address this question, this study examines the application of data mining techniques to a large asthma medical dataset.

A discussion introducing various methods for a smooth approach, straying from the `jack of all trades, master of none' to a modular cooperative approach for a successful outcome is proposed. The results of this study support the use of data mining as a useful tool and highlight the advantages on a global scale of closer relations between the two distinct fields. The exploration of CRISP methodology suggests that a `one methodology fits all approach' is not appropriate, but rather combines to create a hybrid holistic approach to data mining.

About the speaker

Sam Schmidt is an IT professional and the director of a consultancy firm to large multinational companies. He holds a degree in Information Technology, graduating with distinction from Deakin University. He is a member of the Australian Computing Society (ACS) and a weekly participant in the Team for Universal Learning and Intelligent Processing (TULIP). Sam Schmidt has a longstanding interest in machine learning and the fast emerging field of AI/data mining. His latest research publications have identified the benefits of taking a holistic approach to data mining on traditionally researched data sets, and the value this data represents to the community. His background also includes employment with the Deakin Computer Assisted Research Facility (DCARF) and as a casual academic at Deakin University. Sam is currently completing post-graduate research in bioinformatics, using various datamining techniques.