PREDIKSI RISIKO OBESITAS PADA DEWASA MUDA MENGGUNAKAN MACHINE LEARNING METODE DECISION TREE

NURLISTYO, ANANDA KHAIRUNNISA and Liberty, Iche Andriyani and Septadina, Indri Seta (2023) PREDIKSI RISIKO OBESITAS PADA DEWASA MUDA MENGGUNAKAN MACHINE LEARNING METODE DECISION TREE. Undergraduate thesis, Sriwijaya University.

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Abstract

Introduction : Obesity has been designated as a global epidemic due to its increasing prevalence. Obesity is a complex health problem with limited understanding of risk factors. The purpose of this study is to apply, analyze, and evaluate a Machine Learning algorithm, the Decision Tree in predicting obesity in young adults. Method : This type of research is analytic observational with a cross sectional study design. This study is a secondary study. The sample in this study were young adults (20-39 years old) who visited one of the health centers in Palembang City, with a minimum sample size of 262. Univariate and bivariate analysis using SPSS and multivariate using Machine Learning Decision Tree. Results : The total sample was 971 people with the results of 581 people (59.8%) being obese, the majority of respondents were female (73.7%), and the majority of samples aged 35-39 (45%). The results of the analysis with chi-square obtained a significant relationship in the variables of age, gender, and physical activity with a p-value <0.05. Prediction using Decision Tree obtained an accuracy of 67% with the main predictor being physical activity. Conclusion : Decision Tree is less accurate in predicting obesity in young adults. Keywords : Obesity, young adults, machine learning, decision tree

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Obesitas, Dewasa Muda, Machine Learning
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
Divisions:	04-Faculty of Medicine > 11201-Medicine (S1)
Depositing User:	Ananda Khairunnisa Nurlistyo
Date Deposited:	29 Dec 2023 01:22
Last Modified:	29 Dec 2023 01:22
URI:	http://repository.unsri.ac.id/id/eprint/137027

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