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Modern hospitals and clinics produce tons of electronic data every day regarding patients, medications, treatments, and diseases. These amounts of data contain the potential to help humanity understand and analyze the biomedical fields from a statistical and predictive point of view. Throughout the years, research has been concluded to develop methods of interpreting and analyzing this data. Biomedical statistical researchers have experimented algorithms to achieve findings and associations within the aspects of the data given. Medical decision-making is becoming more and more dependent on data analysis, rather than conventional experience and intuition. Hence, this project will look into the feasibility of developing software for hospital data analysis, specifically, the MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care) data that support a diverse range of analytic studies which extend across epidemiology, clinical decision-rule improvement, and electronic tool development. This statistical software is programmed to run multiple algorithms on the massive datasets in the mean of revealing similarities of items related to sets by focusing on the algorithm based of Market-Basket method. With the aim to assist in showing patterns and associations in hospital big data, the software reveals associations and apprehending patterns inside this data demonstrated as predictive analytics that can assist in handling comparable cases and present clinical and hospital-decisions

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Design
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 13 Nov 2015 09:44
Last Modified: 25 Jan 2017 09:35
URI: http://utpedia.utp.edu.my/id/eprint/15997

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