Proving the Associated Factor That Contributing the Caries Status among Pre-School Children in Bachok Kelantan
Wan Muhamad Amir W Ahmad1, Ruhaya Hasan2, Mohd Fadhli Khamis3, Nor Azlida Aleng4, Noraini Mohamad5
1Wan Muhamad Amir W. Ahmad*, School of Dental Sciences, Health Campus, Universiti Sains Malaysia (USM), 16150 Kubang Kerian, Kelantan, Malaysia.
2Ruhaya Hasan, School of Dental Sciences, Health Campus, Universiti Sains Malaysia (USM), 16150 Kubang Kerian, Kelantan, Malaysia.
3Mohd Fadhli Khamis, School of Dental Sciences, Health Campus, Universiti Sains Malaysia (USM), 16150 Kubang Kerian, Kelantan, Malaysia.
4Nor Azlida Aleng, School of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, 21030, Kuala Terengganu, Malaysia.
5Noraini Mohamad, School of Dental Sciences, Health Campus, Universiti Sains Malaysia (USM), 16150 Kubang Kerian, Kelantan, Malaysia.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2038-2042 | Volume-8 Issue-4, November 2019. | Retrieval Number: B2126078219/2019©BEIESP | DOI: 10.35940/ijrte.B2126.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper focuses on the proving of the associated factor for the caries status among preschool children in Bachok, Kelantan. This research paper is mainly focused on the potential factor that most contributing to Early Childhood Caries (ECC). There are two methodologies approaches in this research paper which is Decision Tree Analysis (DTA) and Multi-Layer Perceptron (MLP). The results from both analyses can be used to assist the public and also the stakeholder to control the prevalence of ECC in the future. Results from both analyses are also very useful to redesign the health treatment among pre-school children, to educate the parents, teachers, and to improve the service which offered by the ministry of health from time to time by focusing the most influential factors which lead to ECC in the local community. According to the result of Decision Tree Analysis, the most factor that leads to caries status among preschool children are father’s occupation, household income, children’s weight and the type of water used in their house. While using Multi-Layer Perceptron (MLP) neural networks modeling, the factor can be summarized as household income factor, children’s weight, father occupation and also the type of water used in their house. From the results of the Decision Tree and Multi-Layer Perceptron (MLP) reveals that the top three factors lead to ECC were household income factor, children’s weight, father’s occupation. This information will provide a very useful information to forecast ECC status among preschool children.
Keywords: Decision Tree Analysis, Multilayer Perceptron, Neural Network, Childhood Caries.
Scope of the Article: Personal and Wearable Networks.