Towards an Artificial Intelligence System for Geographical Analysis of Health Data
Journal
European Journal of Geography
Date Issued
October 2013
Author(s)
Abstract
The complexity of modern scientific research requires advanced approaches to handle and
analyse rich and dynamic data. Organizations such as hospitals, hold a great number of health
datasets which may consist of many individual records. Artificial Intelligence methodologies
incorporate approaches for knowledge retrieval and pattern discovery, which have been
proven to be useful for data analysis in various disciplines. Decision trees methods belong to
knowledge discovery methodologies and use computational algorithms for the extraction of
patterns from data. This work describes the development of an autonomous Decision Support
System (“Dth 1.0”) for the real-time analysis of health data with the use of decision trees. The
proposed system uses a patient's dataset based on the patients’ symptoms and other relevant
information and prepares reports about the importance of the characteristics that determine
the number of patients of a specific disease. This work presents the basic concept of decision
trees, describes the design of a tree-based system and uses a virtual database to illustrate the
classification of patients in a hypothetical intra-hospital case study.
analyse rich and dynamic data. Organizations such as hospitals, hold a great number of health
datasets which may consist of many individual records. Artificial Intelligence methodologies
incorporate approaches for knowledge retrieval and pattern discovery, which have been
proven to be useful for data analysis in various disciplines. Decision trees methods belong to
knowledge discovery methodologies and use computational algorithms for the extraction of
patterns from data. This work describes the development of an autonomous Decision Support
System (“Dth 1.0”) for the real-time analysis of health data with the use of decision trees. The
proposed system uses a patient's dataset based on the patients’ symptoms and other relevant
information and prepares reports about the importance of the characteristics that determine
the number of patients of a specific disease. This work presents the basic concept of decision
trees, describes the design of a tree-based system and uses a virtual database to illustrate the
classification of patients in a hypothetical intra-hospital case study.