Title: A predictive model for identifying health trends among Māori and Pacific people - analysis from ten years of New Zealand Public Hospital discharges

Authors: Shaolong Wang; Farhaan Mirza; Mirza Mansoor Baig

Addresses: School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand ' School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand ' School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand

Abstract: Our research was focused on the quality of healthcare services for Māori and Pacific Islanders. We used New Zealand (NZ) Public Hospital discharges data from 2005 to 2015 for our research. A prediction model has been developed to predict the trends for patients with a specific chronic disease, external injuries and operative procedures based on the previous/historic data. Initial exploration suggests that the service demand increased from 138,656 in 2005 to 163,386 in 2015. We successfully analysed the diseases with highest incidence rate and key characteristics of this group of patients. Our research concluded with a series of key findings on the disease types including injuries, procedures, and services.

Keywords: machine learning; Māori and Pacific Islanders; healthcare delivery; disease prediction.

DOI: 10.1504/IJMEI.2021.114886

International Journal of Medical Engineering and Informatics, 2021 Vol.13 No.3, pp.190 - 199

Received: 16 Nov 2018
Accepted: 29 Mar 2019

Published online: 11 May 2021 *

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