{"title":"Comparison of machine learning models to predict complications of bariatric surgery: A systematic review.","authors":"Raoof Nopour","doi":"10.1177/14604582241285794","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background and aim:</b> Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has numerous complications, and early identification of these complications could be essential in assisting patients to have a higher-quality surgery. Machine learning has a significant role in prediction tasks. So far, no systematic review has been carried out on leveraging ML techniques for predicting complications of bariatric surgery. Therefore, this study aims to perform a systematic review for better prediction insight. <b>Materials and methods:</b> This review was conducted in 2023 based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched scientific databases using the inclusion and exclusion criteria to obtain articles. The data extraction form was used to gather data. To analyze the data, we leveraged the narrative synthesis of the quantitative data. <b>Results:</b> Ensemble algorithms outperformed others in large databases, especially at the national registries. Artificial Neural Networks (ANN) performed better than others based on one-single-center database. Also, Deep Belief Networks (DBN) and ANN obtained favorable performance for complications such as diabetes, dyslipidemia, hypertension, thrombosis, leakage, and depression. <b>Conclusion:</b> This review gave us insight into using ensemble and non-ensemble algorithms based on the types of datasets and complications.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241285794"},"PeriodicalIF":2.2000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Informatics Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/14604582241285794","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Background and aim: Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has numerous complications, and early identification of these complications could be essential in assisting patients to have a higher-quality surgery. Machine learning has a significant role in prediction tasks. So far, no systematic review has been carried out on leveraging ML techniques for predicting complications of bariatric surgery. Therefore, this study aims to perform a systematic review for better prediction insight. Materials and methods: This review was conducted in 2023 based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched scientific databases using the inclusion and exclusion criteria to obtain articles. The data extraction form was used to gather data. To analyze the data, we leveraged the narrative synthesis of the quantitative data. Results: Ensemble algorithms outperformed others in large databases, especially at the national registries. Artificial Neural Networks (ANN) performed better than others based on one-single-center database. Also, Deep Belief Networks (DBN) and ANN obtained favorable performance for complications such as diabetes, dyslipidemia, hypertension, thrombosis, leakage, and depression. Conclusion: This review gave us insight into using ensemble and non-ensemble algorithms based on the types of datasets and complications.
背景和目的:由于生活方式的改变,减肥手术在全球范围内不断扩大。然而,这种手术有许多并发症,及早发现这些并发症对于帮助患者接受更高质量的手术至关重要。机器学习在预测任务中发挥着重要作用。迄今为止,还没有关于利用 ML 技术预测减肥手术并发症的系统性综述。因此,本研究旨在进行系统回顾,以获得更好的预测见解。材料和方法:本综述于 2023 年根据《系统综述和元分析首选报告项目》(Preferred Reporting Items for Systematic Reviews and Meta-Analyses,PRISMA)进行。我们使用纳入和排除标准搜索科学数据库,以获取文章。数据提取表用于收集数据。为了分析数据,我们对定量数据进行了叙述性综合。结果在大型数据库中,特别是在国家登记处,集合算法的表现优于其他算法。人工神经网络(ANN)在单中心数据库中的表现优于其他算法。此外,在糖尿病、血脂异常、高血压、血栓形成、渗漏和抑郁等并发症方面,深度信念网络(DBN)和人工神经网络也表现出色。结论这篇综述让我们了解了如何根据数据集和并发症的类型使用集合算法和非集合算法。
期刊介绍:
Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.