rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Rule ensemble classification for school children in Terengganu
  name:
    name part: Nor Saidah Abd Manan
    role:
      role term: creator
    role:
      role term: author
  name:
    name part: Universiti Sultan Zainal Abidin
    name part: Faculty of Informatics and Computing
  type of resource: text
  origin info:
    place:
      place term: my
    date issued: 2020
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xiii,162 leaves ; 31cm.
  abstract: Childhood obesity is proven to increase the risks of early-onset diabetes which leads to cardiovascular risk during adulthood and premature mortality. Its prevalence is increasing at an alarming rate worldwide and imposes an enormous financial burden to the government to treat its related co-morbidities. There is no national survey carried out to determine the prevalence and trend of childhood obesity in Malaysia especially in rural states such as Terengganu. In this thesis, the data collection and classification of childhood obesity among year six school children from two districts in Terengganu; Besut and Kuala Terengganu are discussed. The 4,245 data were collected from two main sources; National Physical Fitness Standard for Malaysian School Children Assessment Program (SEGAK) and a set of distributed questionnaires. An integrated and automated SEGAK data collection and analysis system is proposed in this thesis. The system, which is known as Health Monitoring System (HEMS), is a web-based system developed with an automated data preprocessing using a three-tier system architecture. This thesis will also introduce the use of data mining method by proposing the best classifier for the classification of childhood obesity among year six school children in Terengganu. Various combinations of attribute evaluator and search method for feature selection were used to identify significant factors that can be considered as potential risks that may influence the childhood obesity. Combination of feature selection methods are then tested on different classifiers namely BayesNet, Naive Bayes, J48, IBk and SMO. Using majority voting, the classifiers was combined. Since the result of multi classifier is not satisfied, a rule was applied. The result showed that ensemble classification using Consistency with Genetic Search for feature selection gives the highest accuracy either with or without rule with the percentage of 76.61 % and 75.22%, respectively. This study proved that an…
  table of contents: 1. Introduction -- 2. Literature review -- 3. Methodology -- 4. Results and discussion -- 5. Conclusions and future works
  note: Nor Saidah bt Abd Manan
  note: Thesis (Degree of Master of Science)- Universiti Sultan Zainal Abidin,2020
  note: Includes bibliographical references(leaves 121-134)
  subject:
    name:
      name part: Universiti Sultan Zainal Abidin
    topic: Dissertations
    genre: Faculty of Informatics and Computing
  subject:
    topic: Mathematics
  subject:
    topic: Model theory
  classification: QA76.9 .N67 2020
  record info:
    record content source: UniSZA
    record creation date: 200921
    record change date: 20210503093000.0
    record identifier: 0000099262