Ensemble-based Supervised Learning for Predicting Diabetes Onset
type: CreativeWork
description: The research presented in this thesis aims to address the issue of undiagnosed diabetes cases. The current state of knowledge is that one in seventy people in the United Kingdom are living with undiagnosed diabetes, and only one in a hundred people could identify the main signs of diabetes. Some of the tools available for predicting diabetes are either too simplistic and/or rely on superficial data for inference. On the positive side, the National Health Service (NHS) are improving data recording in this domain by offering health check to adults aged 40 - 70. Data from such programme could be utilised to mitigate the issue of superficial data; but also help to develop a predictive tool that facilitates a change from the current reactive care, onto one that is proactive. This thesis presents a tool based on a machine learning ensemble for predicting diabetes onset. Ensembles often perform better than a single classifier, and accuracy and diversity have been highlighted as the two vital requirements for constructing good ensemble classifiers. Experiments in this thesis explore the relationship between diversity from heterogeneous ensemble classifiers and the accuracy of predictions through feature subset selection in order to predict diabetes onset. Data from a national health check programme (similar to NHS health check) was used. The aim is to predict diabetes onset better than other similar studies within the literature. For the experiments, predictions from five base classifiers (Sequential Minimal Optimisation (SMO), Radial Basis Function (RBF), Naïve Bayes (NB), Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and C4.5 decision tree), performing the same task, are exploited in all possible combinations to construct 26 ensemble models. The training data feature space was searched to select the best feature subset for each classifier. Selected subsets are used to train the classifiers and their predictions are combined using k-Nearest Neighbours al…
abstract: The research presented in this thesis aims to address the issue of undiagnosed diabetes cases. The current state of knowledge is that one in seventy people in the United Kingdom are living with undiagnosed diabetes, and only one in a hundred people could identify the main signs of diabetes. Some of the tools available for predicting diabetes are either too simplistic and/or rely on superficial data for inference. On the positive side, the National Health Service (NHS) are improving data recording in this domain by offering health check to adults aged 40 - 70. Data from such programme could be utilised to mitigate the issue of superficial data; but also help to develop a predictive tool that facilitates a change from the current reactive care, onto one that is proactive. This thesis presents a tool based on a machine learning ensemble for predicting diabetes onset. Ensembles often perform better than a single classifier, and accuracy and diversity have been highlighted as the two vital requirements for constructing good ensemble classifiers. Experiments in this thesis explore the relationship between diversity from heterogeneous ensemble classifiers and the accuracy of predictions through feature subset selection in order to predict diabetes onset. Data from a national health check programme (similar to NHS health check) was used. The aim is to predict diabetes onset better than other similar studies within the literature. For the experiments, predictions from five base classifiers (Sequential Minimal Optimisation (SMO), Radial Basis Function (RBF), Naïve Bayes (NB), Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and C4.5 decision tree), performing the same task, are exploited in all possible combinations to construct 26 ensemble models. The training data feature space was searched to select the best feature subset for each classifier. Selected subsets are used to train the classifiers and their predictions are combined using k-Nearest Neighbours al…
author: Nnamoko, NA
keywords: QA75 Electronic computers. Computer science, RA0421 Public health. Hygiene. Preventive Medicine
date published: 2018-04
date modified: 2026-07-24T06:30:22Z
provider: Liverpool Jon Moores University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Liverpool John Moores University

PropertyValue
property ID: DOI
value: 10.24377/LJMU.t.00008337