Data Mining Techniques for Classification of Childhood Obesity Among Year 6 School Children

Abdullah, Fadzli Syed; Manan, Nor Saidah Abd; Ahmad, Aryati; Wafa, Sharifah Wajihah; Shahril, Mohd Razif; Zulaily, Nurzaime; Amin, Rahmah Mohd; Ahmed, Amran

doi:10.1007/978-3-319-51281-5_47

Cited by 21 publications

(22 citation statements)

References 16 publications

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“…The proposed model uses Decision Trees with three different algorithms: Simple Cart, J47 and NB Tree. Abdullah et al (2016), the study showed a children obesity classification in grade school 6, from two different Malaysia districts. From the information collected, the authors created 4245 full datasets and they applied the classification techniques: Bayesian Networks, Decision Trees, Neural Networks and Support Vector Machines (SVM).…”

Section: Previous Workmentioning

confidence: 92%

“…Based on the previous statements and the literature you can find in many studies working the obesity influence factors, they have implemented several data mining techniques as you can find in (Davila-Payan et al, 2015;Manna and Jewkes, 2014;Adnan and Husain, 2012;Adnan et al, 2010;Dugan et al, 2015;Zhang et al, 2009;Suguna, 2016;Abdullah et al, 2016). Data mining is a discipline that studies massive data sources, with the objective of obtaining new information from it, to support decision making.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Obesity Level Estimation Software based on Decision Trees

De-La-Hoz-Correa¹,

Mendoza-Palechor²,

De-La-Hoz-Manotas³

et al. 2019

Journal of Computer Science

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Section: Previous Workmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Obesity Level Estimation Software based on Decision Trees

De-La-Hoz-Correa¹,

Mendoza-Palechor²,

De-La-Hoz-Manotas³

et al. 2019

Journal of Computer Science

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show abstract

“…This kind of validation was used because, in general, it is recommended for estimation accuracy (even if computational power enables the use of more folds) due to its relatively low bias and variance [62]. Moreover, the J48 algorithm was selected since it was proven in previous studies to have performed better than other algorithms [63][64][65][66].…”

Section: Methodsmentioning

confidence: 99%

PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

et al. 2019

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Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables.

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“…In [4], various feature selection strategies have been used for the classification of childhood obesity. The data is accrued from Standard Kecergasan Fizikal Kebangs aanuntuk Murid Sekolah Malaysia (SEGAK) Assessment Program and the study questionnaire on socio demographic, physical activity and nutritional assessment.…”

Section: Related Workmentioning

confidence: 99%

“…Moderate [4] This study was to identify the factors that influence the childhood obesity using various feature selection techniques.…”

Section: C50 Decision Treementioning

confidence: 99%

Prediction of Child Development using Data Mining Approach

Bansod¹,

Amonkar²,

Naik³

et al. 2020

IJCA

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Child development is the sector that consists of scientific research of the patterns of growth, power and change that arise from conception through adolescence. One can apply this knowledge to realize the necessities of a child by viewing how and why individuals alternate and grow. Thus, pleasing them and allowing them to arrive at their maximum capacity. The intention of this study is to research the child growth based on the features consisting of age, height and weight. In order to understand how the physical growth of child switches with time, data is gathered from various sources such as Anganwadis, Primary Schools and Primary health Centres and a data mining method is implemented to expect the child growth. In this method, assessment of two data mining approaches ID3 Decision Tree and Naïve Bayes classifier is carried out on the basis of factors such as prediction accuracy, error rate and learning time.

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Data Mining Techniques for Classification of Childhood Obesity Among Year 6 School Children

Cited by 21 publications

References 16 publications

Obesity Level Estimation Software based on Decision Trees

Obesity Level Estimation Software based on Decision Trees

PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

Prediction of Child Development using Data Mining Approach

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