Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements

Gupta, Mehak; Phan, Thao-Ly T.; Bunnell, Timothy; Beheshti, Rahmatollah

doi:10.1145/3506719

Cited by 42 publications

(23 citation statements)

References 63 publications

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“…Predictive models have recently shown much promise in predicting future health outcomes in a variety of biomedical applications. This trend has also been witnessed in a number of non-biomedical areas [5,6]. Due to the fast expansion of big data in the medical domain, clinical prediction models have become more common and widely used.…”

Section: Literature Reviewmentioning

confidence: 98%

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Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Alsareii¹,

Awais²,

Alamri³

et al. 2023

Computer Systems Science and Engineering

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Obesity is a critical health condition that severely affects an individual's quality of life and well-being. The occurrence of obesity is strongly associated with extreme health conditions, such as cardiac diseases, diabetes, hypertension, and some types of cancer. Therefore, it is vital to avoid obesity and or reverse its occurrence. Incorporating healthy food habits and an active lifestyle can help to prevent obesity. In this regard, artificial intelligence (AI) can play an important role in estimating health conditions and detecting obesity and its types. This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset. This dataset is in the form of electronic health records (EHR) containing data on several aspects of daily living, such as dietary habits, physical conditions, and lifestyle variables for various participants with different health conditions (underweight, normal, overweight, and obesity type I, II and III), expressed in terms of a variety of features or parameters, such as physical condition, food intake, lifestyle and mode of transportation. Three classifiers, i.e., eXtreme gradient boosting classifier (XGB), support vector machine (SVM), and artificial neural network (ANN), are implemented to detect the status of several conditions, including obesity types. The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods, achieving overall performance rates of 98.5% and 99.6% in the scenarios explored.

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Section: Literature Reviewmentioning

confidence: 98%

“…As stated above, obesity is a well-documented cause of significant health risks, including type 2 diabetes, coronary heart disease, stroke, etc. [3,5]. Obesity also results in psychological issues.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Alsareii¹,

Awais²,

Alamri³

et al. 2023

Computer Systems Science and Engineering

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“…Cervantes et al developed decision tree (DT), k -means, and support vector machine (SVM)-based data mining techniques to identify obesity levels among young adults between 18 and 25 years of age so that interventions could be undertaken to maintain a healthier lifestyle in the future [ 23 ]. Gupta et al developed a deep learning model (long short-term memory (LSTM)), which predicted obesity between 3 and 20 years of age with 80% accuracy using unaugmented electronic health record (EHR) data from 1 to 3 years prior [ 24 ]. Marcos-Pasero used random forest (RF) and gradient boosting to predict the BMI from 190 multidomain variables (data collected from 221 children aged 6 to 9 years) and determined the relative importance of the predictors [ 25 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers

Mondal

Foysal

Norman

2023

Sensors

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Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child’s body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child’s current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child’s growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child’s obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare.

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“…The simplest way of dealing with this is by deleting the records that contain at least one missing value and thus selecting a subset of the original dataset where there are no missing values [17]- [20]. Gupta et al [21] studied the case of obesity prediction with EHR data using common machine learning models such as random forests and LSTMs. When it comes to missing values, they dropped rows with missing or corrupt values, e.g., implausible dates.…”

Section: Approaches Towards Missing Value Imputation Employingmentioning

confidence: 99%

Missing Value Imputation Methods for Electronic Health Records

et al. 2023

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Electronic health records (EHR) are patient-level information, e.g., laboratory tests and questionnaires, stored in electronic format. Compared to physical records, the EHR alternative allows patients to access their data easily and helps staff with management procedural tasks such as information sharing across different organizations. Moreover, this type of data is commonly used by researchers for predictive and classification purposes, employing statistical and machine learning methods. However, missingness is a phenomenon that is observed very frequently for such measurements. Even though this missingness is often significant, it is usually treated poorly with either case deletion or simple methods, resulting in suboptimal and/or inaccurate predictive results. This happens because the simple methods, e.g., k-nearest neighbors (kNN) and mean/mode imputation, fail in most cases to incorporate the complex relationships that define these medical datasets. To address these limitations, in this paper we test and improve state-of-the-art missing data imputation models and practices. We propose a new missing value imputation method based on denoising autoencoders (DAE) with kNN for the pre-imputation task. We optimize the training methodology by re-applying kNN to the missing data every N epochs using a different value for the variable k each time to yield more accurate results. We also revise a state-of-the-art missing data imputation approach based on a generative adversarial network (GAN). Using this as a baseline, we introduce improvements regarding both the architecture and the training procedure. These models are compared with the ones usually employed within clinical research studies for both the task of imputation and post-imputation prediction. Results show that our proposed deep learning approaches outperform the standard baselines, yielding better imputation and predictive results.

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Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements

Cited by 42 publications

References 63 publications

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers

Missing Value Imputation Methods for Electronic Health Records

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