A framework for childhood obesity classifications and predictions using NBtree

Adnan, Muhamad Hariz Muhamad; Husain, Wahidah; Rashid, Nur’Aini Abdul

doi:10.1109/cita.2011.5999502

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…The authors of [11] examined childhood obesity by collecting data from a variety of sources and identified a few risk factors contributing to childhood obesity and proposed a model to predict childhood obesity using NBtree.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Machine Learning Model for Estimation of Obesity Levels

Choudhuri

2022

Preprint

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Obesity has always been a problem which has plagued humans for many generations, which, since the 1975, almost doubled to turn into a global epidemic. The current human dependence on technology has contributed to the problem even more, with the effects visibly pronounced in late teenagers and early adults. Researchers till date, have tried numerous ways to determine the factors that cause obesity in early adults. On that frontier, our hybrid machine-learning model uses the help of some supervised and unsupervised data mining methods like Extremely Randomized Trees, Multilayer Perceptron and XGBoost using Python to detect and predict obesity levels and help healthcare professionals to combat this phenomenon. Our dataset is a publicly available dataset in the UCI Machine Learning Repository, containing the data for the estimation of obesity levels in individuals from the countries of Mexico, Peru, and Colombia, based on their eating habits and physical condition. The proposed model heavily utilizes feature engineering methods and introduces the concept of a hybrid model. This work has shown improved results over prior works and extensive studies have been undertaken to preserve the robustness of this model.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Machine Learning Model for Estimation of Obesity Levels

Choudhuri

2022

Preprint

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“…A model was presented by [12], which uses fuzzy signature to understand and manage intricacies on children obesity dataset and a solution that could handle the related risk with children motor development and early obesity. [13] Had an initial method to predicting obesity which was developed with the information collected from primary sources: Parents, caretakers and the children themselves. The paper authors tries to identify the risk factors like the obesity, education level of the parents, habits, lifestyle of the children and the environment influence on children.…”

Section: IVmentioning

confidence: 99%

Obesity prediction using machine learning techniques

Musa

Basaky

E.O

2022

J. Appl. Artif. Intell.

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Currently, safeguarding the community is vital in terms of finding solution to health related problems which can be achieved through medical research using the advent of technology. Obesity has become worldwide health concern as it is becoming a threat to the future. It is the most common health problems all over the world. Thousands of diseases as well as risks and death are associated to it. An early prediction of a disease will help both doctors and patients to act and minimize if not total eradication of the root cause or work on preventing the disease symptom from further deterioration. Going through patient’s medical history is one of the methods of identifying a disease which most time consuming as processing manually and it comes with an error-prone analyses and expense. Therefore, there is need to scientifically develop a predicting model of the occurrence of the disease or its existence using an automated technique as it is becoming a need of the day. In this research work, we used machine learning techniques on a public clinical available dataset to predict obesity status using different machine learning algorithms. Five machine learning algorithms were applied. Gboost Classifier, Random Forest Classifier, Decision Tree Classifier, K-Nearest Neighbor and Support Vector Machine and the model has shown promising results with as Gboost classifier achieves the highest accuracy of 99.05% as compared to other classifiers. Meanwhile, the K-Nearest Neighbor gave the relatively strong accuracy of 95.74%.

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“…MuhamadHarizMuhamad Adnan et.al. [6]has proposed Framework for Childhood Obesity Classifications and Predictions using NBtree where the identification of risk Factors such as parental obesity and education, children lifestyle and habits, and environment influences are studied. Daniel Jachyraet.…”

Section: Literature Surveymentioning

confidence: 99%

Data mining approach for diagnose of anxiety disorder

D'monte¹,

Panchal²

2015

International Conference on Computing, Communication &Amp; Automation

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Themental disorder is one of the top five reasons for the cause of death in world and suicide is the third leading cause of death among young adults worldwide. Due to the unawareness and social embarrassment, this disease is normally undiagnosed. Study says that one of every four Indians affected by anxiety disorder, out of which 10% depressed. The reasons affecting the mental health of the Indian patients need to be identified through which better cognitive behavioral therapy can be given by the psychiatrist. Data mining is a way through which Long term monitoring of the patient can be carried out by using stress monitoring test and actual reason affecting the mental health of the Indian patient can be studied via digging through and analyzing enormous sets of data and then extracting the meaning of the data such as their lifestyle, habits etc. It will also provide healthcare professionals an additional source of knowledge for making decisions. The aim of this research is to propose new approach using data mining techniques to predict the stress level of a patient using logistic model tress and to find out different factors affecting the mental health of a Indian patient in an efficiently and an economically faster manner.

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A framework for childhood obesity classifications and predictions using NBtree

Cited by 10 publications

References 13 publications

A Hybrid Machine Learning Model for Estimation of Obesity Levels

A Hybrid Machine Learning Model for Estimation of Obesity Levels

Obesity prediction using machine learning techniques

Data mining approach for diagnose of anxiety disorder

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