Modeling Obesity Using Abductive Networks

Abdel-Aal, R.E.; Mangoud, Abdallah M

doi:10.1006/cbmr.1997.1460

Cited by 17 publications

(5 citation statements)

References 41 publications

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“…The second purpose was to identify important bio-clinical measurements that can be considered as potential risk factors for obesity. This study differs from other ML based works from the literature (e.g., [15][16][17][18]) along two important lines. Firstly, previous works consider BMI as one of the features; however, we excluded this feature since it is an obvious determinant of obesity and ensures a near perfect accuracy.…”

Section: Principal Findingsmentioning

confidence: 75%

“…Previous studies ( [15][16][17][18]) used a limited number of physio-clinical biomarkers to model obesity and its related risk factors. There are a few studies that have considered socio-economic factors, cultural impact, dietary habits, and psychological status from the Qatari population to determine how such factors might lead to obesity [13,[19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors

et al. 2020

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Obesity is an emerging public health problem in the Western world as well as in the Gulf region. Qatar, a tiny wealthy county, is among the top-ranked obese countries with a high obesity rate among its population. Compared to Qatar’s severity of this health crisis, only a limited number of studies focused on the systematic identification of potential risk factors using multimodal datasets. This study aims to develop machine learning (ML) models to distinguish healthy from obese individuals and reveal potential risk factors associated with obesity in Qatar. We designed a case-control study focused on 500 Qatari subjects, comprising 250 obese and 250 healthy individuals- the later forming the control group. We obtained the most extensive collection of clinical measurements for the Qatari population from the Qatar Biobank (QBB) repertoire, including (i) Physio-clinical Biomarkers, (ii) Spirometry, (iii) VICORDER, (iv) DXA scan composition, and (v) DXA scan densitometry readings. We developed several machine learning (ML) models to distinguish healthy from obese individuals and applied multiple feature selection techniques to identify potential risk factors associated with obesity. The proposed ML model achieved over 90% accuracy, thereby outperforming the existing state of the art models. The outcome from the ablation study on multimodal clinical datasets revealed physio-clinical measurements as the most influential risk factors in distinguishing healthy versus obese subjects. Furthermore, multiple feature ranking techniques confirmed known obesity risk factors (c-peptide, insulin, albumin, uric acid) and identified potential risk factors linked to obesity-related comorbidities such as diabetes (e.g., HbA1c, glucose), liver function (e.g., alkaline phosphatase, gamma-glutamyl transferase), lipid profile (e.g., triglyceride, low density lipoprotein cholesterol, high density lipoprotein cholesterol), etc. Most of the DXA measurements (e.g., bone area, bone mineral composition, bone mineral density, etc.) were significantly (p-value < 0.05) higher in the obese group. Overall, the net effect of hypothesized protective factors of obesity on bone mass seems to have surpassed the hypothesized harmful factors. All the identified factors warrant further investigation in a clinical setup to understand their role in obesity.

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Section: Principal Findingsmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors

et al. 2020

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“…Machine learning represents a powerful set of algorithms that can characterize, adapt, learn, predict and analyse data, amplifying our understanding of obesity and our capacity to predict with unprecedented precision. To this end, there have been increasing applications of machine learning in the obesity research field . To demonstrate the effectiveness of machine learning for a broadly trained interdisciplinary readership, we provide here a general description of several of the most recognized methods along with a history of previous successful applications.…”

Section: Introductionmentioning

confidence: 99%

A review of machine learning in obesity

et al. 2018

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Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.

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“…More recently, polynomial network software programs have been classed as data mining tools (Agarwal, 1999;Kim, 2002;King et al, 1998;and Pyo et al, 2002). Polynomial networks have been used for a wide range of modeling applications, including defense (Montgomery et al, 1990), financial (Stepanov, 1974;Kim, 2002), medical (Abdel-Aal and Mangoud, 1997;Griffin et al, 1994), process control (Silis and Rozenblit, 1976), and agriculture (Duffy and Franklin, 1975;Ivakhnenko et al, 1977;Lebow et al, 1984;Pachepsky and Rawls, 1999;Reddy and Pachepsky, 2000).…”

Section: Nonlinear Polynomial Network Modelingmentioning

confidence: 99%

Comparative Models of Hydrocarbon Emissions for a Diesel Engine Operating at Constant Loads and Speeds

Watson¹,

Bostic²,

Harrison³

2009

Transactions of the ASABE

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Linear multiple regression (LMR) and nonlinear polynomial network (NPN) models were developed from data collected from ISO 8178-4 (1996) test cycle B-type tests (ISO) and an expanded set of tests (EXP) to predict hydrocarbon (HC) emissions from a diesel engine. LMR using the ISO training data (R 2 = 0.94) resulted in overfitting of the model as applied to the evaluation data (R 2 = 0.49). LMR based on the expanded data (R 2 = 0.68) was a better LMR model when applied to the evaluation data (R 2 = 0.64). NPN using the expanded training data (R 2 = 0.99) resulted in the best model when applied to the evaluation data (R 2 = 0.98) and is preferred for predicting HC when the larger set of test mode data are available. NPN using the ISO training data (R 2 = 0.99) resulted in a satisfactory fit for the evaluation data (R 2 = 0.91), although with a higher average absolute error (0.52 vs. 0.42 g/kWh) than NPN using the EXP training data. This model was also considered suitable for predicting HC. Results of this initial study suggest that data could be collected during ISO 8178-4 emission tests and modeled with NPN to predict HC emissions for a diesel engine operating at various constant speeds and loads.

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Modeling Obesity Using Abductive Networks

Cited by 17 publications

References 41 publications

Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors

Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors

A review of machine learning in obesity

Comparative Models of Hydrocarbon Emissions for a Diesel Engine Operating at Constant Loads and Speeds

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