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Nnamoko, NA, Hussain, A and England, D (2018) Predicting Diabetes Onset: an Ensemble Supervised Learning Approach. In: 2018 IEEE Congress on Evolutionary Computation (CEC) . (IEEE Congress on Evolutionary Computation, 08 July 2018 - 13 July 2018, Brazil).
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Abstract
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) |
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| Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| 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 |
| Date Deposited: | 16 Apr 2018 09:37 |
| Last Modified: | 12 Jun 2024 14:39 |
| DOI or ID number: | 10.1109/CEC.2018.8477663 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/8488 |
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