A POI-Based Machine Learning Method for Predicting Residents’ Health Status

Cao, Shicong; Zheng, Hao

doi:10.1007/978-981-16-5983-6_13

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Without a deep learning approach, spatial models could not be directly used to analyze GIS data to study obesity ( 26 ). In this article, we recommend the GWmodel, which is a widely used R-based package that implements geographically weighted models for exploring spatial heterogeneity ( 27 ). A classic use case of the application of the GWmodel can be found in a recent study in which a geographically weighted regression (GWR) model (based on the GWmodel package) was adopted to analyze the relationship between socioeconomic factors, obesity, and air temperature and unhealthy behaviors in the USA ( 28 ).…”

Section: Resultsmentioning

confidence: 99%

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

2022

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Research on obesity and related diseases has received attention from government policymakers; interventions targeting nutrient intake, dietary patterns, and physical activity are deployed globally. An urgent issue now is how can we improve the efficiency of obesity research or obesity interventions. Currently, machine learning (ML) methods have been widely applied in obesity-related studies to detect obesity disease biomarkers or discover intervention strategies to optimize weight loss results. In addition, an open source of these algorithms is necessary to check the reproducibility of the research results. Furthermore, appropriate applications of these algorithms could greatly improve the efficiency of similar studies by other researchers. Here, we proposed a mini-review of several open-source ML algorithms, platforms, or related databases that are of particular interest or can be applied in the field of obesity research. We focus our topic on nutrition, environment and social factor, genetics or genomics, and microbiome-adopting ML algorithms.

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Section: Resultsmentioning

confidence: 99%

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

2022

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“…Recent studies on image-to-image translation have identified that such GAN can synthesize pictures from label maps very effectively (Isola et al, 2017). Following this investigation, many researchers have used GAN to help plan for the future by predicting certain factors in the city, such as crime rates (He and Zheng, 2021), residents' health status (Cao and Zheng, 2021), and short-term passenger flow on rails (Li et al, 2022). Therefore, machine learning can help shape a better-built environment for workers by generating reasonable amenities distributions efficiently based on a city map.…”

Section: New Potentials Offered By Machine Learning Methods In Amenit...mentioning

confidence: 99%

Predicting Amenities Distributions for Workers from the Built Environment Based on Machine Learning

Wan¹,

Pan²,

Xue³

et al. 2023

CAADRIA Proceedings

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The working population has increased in cities with urbanization. Providing a supportive built environment with reasonable amenities distribution for them is becoming more important. Previous GIS-based approaches to urban planning for this issue tend to be subjective with high labour costs. This paper uses the generative adversarial network (GAN) to explore the relationship between amenities distributions and urban morphology, thus effectively predicting and visualizing the ideal amenities distributions in fastgrowing cities based on the condition of well-developed megacities. In this research, we take Shanghai, one of the global cities in China with a big labour market, as the research sample. First, we use the Point of Interest (POI) data to draw the heatmap of urban amenities that support workers' daily life and collect the corresponding city maps. Then, we cut them into hundreds of image pairs as the training set and train a GAN model for predicting the future amenities distributions in other cities. To implement the model, we further collect the city maps of Jiaxing, one of the second-tier cities near Shanghai, as the testing set. Results show that our trained model can accurately predict amenities distributions for its future. The GAN-based prediction could effectively support detailed urban planning.

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“…This study serves as a classic example of the application of the GWmodel. obesity assessment Hu, et al, 2022, Kamel, et al, 2019, Cao &Zheng, 2022, Lotfata, 2022, Lambert, et al, 2021 Genetics is the study of genes and how certain traits or disorders are handed down through generations. The community is given access to an open platform for PGS research thanks to the polygenic score (PGS) database, which compiles published PGS information.…”

Section: Geographic Information System (Gis) Modelmentioning

confidence: 99%

Application of Machine Learning Algorithm and Artificial Intelligence in Improving Metabolic Syndrome related complications: A review

Ghosh,

Choudhury,

Singh

et al. 2024

IJALSR

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Aim: This review provides a concise summary of the utilisation of artificial intelligence (AI) in the context of metabolic diseases and their impact on overall well-being. The primary emphasis is placed on exploring the potential applications and addressing the issues associated with employing AI-based methodologies for both research purposes and clinical treatment in the context of non-communicable diseases. Methods: The relevant published publications were summarised by conducting computerised literature searches on several reputable databases using specific keywords such as MS, Artificial Intelligence (AI), Machine Learning (ML), Coronary Heart Disease, Obesity, and dyslipidemia. The researchers picked papers that had unique data and integrated the significant findings from these studies into the conclusion, which pertains to the present state of Metabolic Syndrome. Results: In summary, although the utilisation of artificial intelligence in educational interventions shows potential, it is important to acknowledge its inherent limits. Although there is a growing body of literature on the utilisation of digital and intelligent tools in the management of MS, a significant proportion of relevant studies suffer from limitations such as insufficient sample sizes or a failure to establish the clinical significance of the tested interventions. Notwithstanding these challenges, the advancements in utilising artificial intelligence (AI) in the field of medicine have been rapidly evolving, and it is imperative to acknowledge the potential and scholarly significance of these applications. Conclusion: The integration and comprehensive utilisation of certain artificial intelligence (AI) technologies can enable future health education on MS to provide comprehensive, personalised, and intelligent training. This intervention will provide patients with enduring protection and ongoing guidance throughout their lives.

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A POI-Based Machine Learning Method for Predicting Residents’ Health Status

Cited by 5 publications

References 6 publications

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

Predicting Amenities Distributions for Workers from the Built Environment Based on Machine Learning

Application of Machine Learning Algorithm and Artificial Intelligence in Improving Metabolic Syndrome related complications: A review

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