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Predicting Diabetes Onset: an Ensemble Supervised Learning Approach

Nnamoko, NA, Hussain, A and England, D Predicting Diabetes Onset: an Ensemble Supervised Learning Approach. In: IEEE Congress on Evolutionary Computation, 08 July 2018 - 13 July 2018, Brazil. (Accepted)

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An exploratory research is presented to gauge the impact of feature selection on heterogeneous ensembles. The task is to predict diabetes onset with healthcare data obtained from UC Irvine (UCI) database. Evidence suggests that accuracy and diversity are the two vital requirements to achieve good ensembles. Therefore, the research presented in this paper exploits diversity from heterogeneous base classifiers; and the optimisation effect of feature subset selection in order to improve accuracy. Five widely used classifiers are employed for the ensembles and a meta-classifier is used to aggregate their outputs. The results are presented and compared with similar studies that used the same dataset within the literature. It is shown that by using the proposed method, diabetes onset prediction can be done with higher accuracy.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions: Computer Science & Mathematics
Publisher: IEEE Publishing
Date Deposited: 16 Apr 2018 09:37
Last Modified: 13 Apr 2022 15:16
URI: https://researchonline.ljmu.ac.uk/id/eprint/8488
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