Advancements in AI for Obesity Prediction: A Systematic Review and Meta-Analysis
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Abstract
Background: The worldwide prevalence of obesity has reached an epidemic level and has presented a significant burden to public health infrastructure. There is a critical need for novel predictive instruments, and computerized intelligent systems (artificial intelligence or AI), specifically machine learning (ML), are an appealing method to precisely forecast obesity and associated health outcomes. Nonetheless, an extensive summary of recent evidence on the efficacy and usage of these models remains a critical void within literature. Aim and Objective: The principal research question to be addressed is: “What is the overall predictive efficacy of computer and machine-based intelligence systems in the prediction of obesity and overweight status in adult and adolescent populations?” Methods: Systematic review and meta-analysis were conducted on articles released in the time period 2020 to 2025. The databases used in this review were PubMed, Scopus, Web of Science, and Embase. The relevant literature was searched by using the application of keywords and Boolean operators, namely "obesity," "overweight," "artificial intelligence," "machine learning," "deep learning," and "prediction." The total of ten studies were covered in the systematic review while four studies were chosen to be used in the meta-analysis. The quality of the studies were assessed by using Newcastle–Ottawa Scale (NOS). A meta-analysis was undertaken using a random-effects model to calculate the pooled effect size, standard error, and 95% confidence interval with statistical analysis being done in RStudio. Results: A total of 307 studies were identified by the database searching. 92 duplicates and 215 abstracts were removed by screening. The full systematic review covered 10 studies. The meta-analysis in four of these studies with a pooled sample size of 363,731 produced a pooled effect size (proportion) of 0.730 (95% CI = 0.719 to 0.741) demonstrating a strong degree of predictive accuracy. Conclusion: Artificial intelligence and machine learning models consistently exhibit superior predictive capabilities in estimating obesity and overweight conditions. The results underscore the clinical significance of these models as essential instruments for early prevention and intervention efforts, providing an accurate methodology to tackle the public health challenge posed by obesity.
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Copyright (c) 2025 Veerakesari S, Sarathlal S, Aleena Thomas Cheeran, Noula Rahim, Jamila Hameed
This work is licensed under a Creative Commons Attribution 4.0 International License.
Creative Commons License All articles published in Annals of Medicine and Medical Sciences are licensed under a Creative Commons Attribution 4.0 International License.
Veerakesari S, Department of General Medicine, Karuna Medical College, Vilayodi, Chittur, Palakkad, Kerala, India.
Department of General Medicine, Karuna Medical College, Vilayodi, Chittur, Palakkad, Kerala, India.
Sarathlal S, Department of General Medicine, Karuna Medical College, Vilayodi, Chittur, Palakkad, Kerala, India.
Department of General Medicine, Karuna Medical College, Vilayodi, Chittur, Palakkad, Kerala, India.
Aleena Thomas Cheeran, Department of General Medicine, Karuna Medical College, Vilayodi, Chittur, Palakkad, Kerala, India.
Department of General Medicine, Karuna Medical College, Vilayodi, Chittur, Palakkad, Kerala, India.
Noula Rahim, Department of Biochemistry, Karuna Medical College, Vilayodi, Chittur, Palakkad, Kerala, India.
Department of Biochemistry, Karuna Medical College, Vilayodi, Chittur, Palakkad, Kerala, India.
Jamila Hameed, Department of Obstetrics and Gynaecology, Karuna Medical College, Vilayodi, Chittur, Palakkad, Kerala, India.
Research Mentor, Emiratus Professor, Department of Obstetrics and Gynaecology, Karuna Medical College, Vilayodi, Chittur, Palakkad, Kerala, India.
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