Earticle

초록

영어

Even by using the data mining, many weaknesses still existed in childhood obesity prediction and it is still far from achieving perfect prediction. This paper studies previous steps involved in childhood obesity prediction using different data mining techniques and proposed hybrid approaches to improve the accuracy of the prediction. The steps taken in this study were a review of childhood obesity, data collections, data cleaning and preprocessing, implementation of the hybrid approach, and evaluation of the proposed approach. The hybrid approach consists of the classification and regression tree, Naïve Bayes, mean value identification and Euclidean distances classification. The results from the evaluation have shown that the proposed approach has 60% sensitivity for childhood obesity prediction and 95% sensitivity for childhood overweight prediction.

목차

Abstract
 1. Introduction
 2. Data Mining Techniques
  2.1. Classification and Regression Tree
  2.2. Naive Bayes
  2.3. Euclidean Distances
 3. Proposed Hybrid Approaches
  3.1. The First Approach
  3.2. The Second Approach
 4. Experimental
  4.1. Materials and Methods
 5. Results and Discussion
  5.1. The First Approach
  5.2. The Second Approach
  5.3. A Comparison using ROC Curve
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Muhamad Hariz Muhamad Adnan [ School of Computer Sciences, Universiti Sains Malaysia ]
• Wahidah Husain [ School of Computer Sciences, Universiti Sains Malaysia ]
• Nur'Aini Abdul Rashid [ School of Computer Sciences, Universiti Sains Malaysia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605