Machine Learning Approach of Obesity Level Classification - A Systematic Literature Review of Methods and Factors

Aug 3, 2026

Rising obesity prevalence increases the likelihood of chronic diseases such as diabetes, heart disease, and cancer, creating strong demand from health authorities, researchers, and the public for comprehensive solutions. This systematic literature review examines studies from 2018–2023, emphasizing risk factors associated with obesity and the use of machine learning to detect obesity-related risk, guided by the SPAR-4-SLR approach. Findings summarize key risk factors, machine learning prediction methods, and model optimization strategies to support evidence-based understanding of obesity and ML-driven interventions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Obesity as a multifactorial health condition

Global prevalence and trends

Rationale for systematic review

Objectives and scope

Review period: 2018–2023

Focus: risk factors and ML-based detection

Machine Learning Approach of Obesity Level Classification: A Systematic Literature Review of Methods and Factors Steven Marcelino Tandiono1 , Samuel Ady Sanjaya2􀀍 1, 2 Information Systems Department, Faculty of Engineering and Informatics, Multimedia Nusantara University, Indonesia | Informasi Artikel
Riwayat Artikel
Diserahkan : 02-12-2023
Direvisi : 08-12-2023
Diterima : 12-12-2023
Kata Kunci:
Faktor Risiko, Obesitas, Machine Learning,
Penyakit, Tinjauan Literatur Sistematis
Keywords :
Risk Factors, Obesity, Machine Learning, Disease, Systematic Literature Review | ABSTRAK | | --- | --- | | | Meningkatnya prevalensi obesitas, yang memperbesar risiko penyakitseperti diabetes, penyakit jantung, dan kanker, mendorong kekhawatiran global. Otoritas kesehatan, peneliti, dan masyarakat menuntut solusi komprehensif. Machine learning menjanjikan dalam memproyeksikan risiko obesitas dengan mengidentifikasi penyebabnya secara akurat. Penelitian ini mereview literatur dari 2018-2023, fokus pada faktor-faktor yang berpengaruh terhadap obesitas dan aplikasi machine learning dalam mendeteksi risikonya, menggunakan pendekatan SPAR-4-SLR untuk panduan sistematis. Temuan ini menyoroti faktor risiko utama dan metode machine learning untuk prediksi, serta strategi optimalisasi model prediksi. Dengan memahami hubungan antara faktor risiko , obesitas dan pendekatan penyelesaian dengan machine learning, penelitian ini bertujuan untuk mengidentifikasi, mengevaluasi, dan mensintesis literatur yang relevan dalam suatu bidang penelitian untuk menyediakan pemahaman komprehensif tentang topik tersebut. | | | ABSTRACT | | | The increasing prevalence of obesity, which amplifies the risk of diseases such as diabetes, heart disease, and cancer, has raised global concerns. Health authorities, researchers, and the community demand comprehensive solutions. Machine learning holds promise in projecting obesity risks by accurately identifying its causes. This study reviews literature from 2018-2023, focusing on factors influencing obesity and the application of machine learning in risk detection, using the SPAR-4-SLR approach for systematic guidance. The findings highlight key riskfactors and machine learning methods for prediction, as well as optimization strategies for predictive models. By understanding the relationship between risk factors, obesity, and machine learning-based solutions, this research aims to identify, evaluate, and synthesize relevant literature in a specific research field to provide a comprehensive understanding of the topic. | | Corresponding Author :
Samuel Ady Sanjaya
Information Systems Department, Faculty of Engineering of Informatics, Multimedia Nusantara University
Jl. Scientia Boulevard, Curug Sanereng, Kec. Klp. Dua, Kabupaten Tangerang, Banten 15811 Email: [samuel.ady@umn.ac.id](samuel.ady@umn.ac.id) | | INTRODUCTION Obesity is a complex health condition that not only reduces overall body quality, but also increases susceptibility to life-threatening chronic diseases. The World Health Organization (WHO), in its official website, defines obesity as the excessive accumulation offat, which presentsa dangerous risk to one's health. This excessive accumulation offat is not just aesthetic, but also a significant precursor to various chronic diseases. The implications of obesity go far beyond the physical aspect, permeating the intricate web of metabolic, cardiovascular, and respiratory systems, thus increasing the urgency for comprehensive understanding, prevention, and management of this pervasive health problem (WHO, 2023) . Furthermore, WHO also states that the underlying cause of obesity is an energy imbalance between calories consumed and calories expended; as a result, the body stores unused calories in the form offat. This health problem can be experienced by individuals of all ages, regardless of age, gender, or socioeconomic status (Solomon et al. , 2023) . Therefore, unders

FAQs

Why is obesity a serious public health concern?

Obesity is linked to higher susceptibility to chronic diseases such as diabetes, heart disease, and cancer, and it reflects a harmful energy imbalance that affects metabolic and physiological systems.

What time range and method does the review use?

The review covers studies published between 2018 and 2023 and applies the SPAR-4-SLR approach to provide systematic guidance for literature searching and synthesis.

Which aspects of obesity does the review emphasize?

The review focuses on factors that influence obesity risk and on machine learning methods used to detect or predict that risk, including strategies for optimizing predictive models.

Aug 3, 2026

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