Machine Learning Approach for the Early Prediction of the Risk of Overweight and Obesity in Young People

Obesity is a major global concern with more than 2.1 billion people overweight or obese worldwide which amounts to almost 30% of the global population. If the current trend continues, the overweight and obese population is likely to increase to 41% by 2030. Individuals developing signs of weight gain or obesity are also at a risk of developing serious illnesses such as type 2 diabetes, respiratory problems, heart disease and stroke. Some intervention measures such as physical activity and healthy eating can be a fundamental component to maintain a healthy lifestyle. Therefore, it is absolutely essential to detect childhood obesity as early as possible. This paper utilises the vast amount of data available via UK’s millennium cohort study in order to construct a machine learning driven model to predict young people at the risk of becoming overweight or obese. The childhood BMI values from the ages 3, 5, 7 and 11 are used to predict adolescents of age 14 at the risk of becoming overweight or obese. There is an inherent imbalance in the dataset of individuals with normal BMI and the ones at risk. The results obtained are encouraging and a prediction accuracy of over 90% for the target class has been achieved. Various issues relating to data preprocessing and prediction accuracy are addressed and discussed

Statistical Analysis of Health Habits for Incoming College Students

Health habits among college students are commonly overseen, especially for students transitioning from high school right into college. These students are becoming independent young adults, and learning how to adapt to a different scenery when it comes to their learning environment. As these young adults transition into college, this is the perfect time for the students to become more vulnerable and comfortable with their independence, and their weight begins to fluctuate. Many variables come into consideration when increasing weight as an incoming first-year student. Students are more likely to live alone, get a job, and rely on fast food and quick snacks, all while juggling school, which may also lead to stress and binge eating. Not only may their stress lead to binge eating, but it may also include alcohol consumption as well as Marijuana consumption. This topic is essential because if unmeasured, weight gain may lead to health problems, which may also interfere with the student’s learning. It is also crucial because making students aware of their surroundings and possible outcomes may prevent them from gaining substantial unhealthy weight. Our study consists of about 900 students from a large Hispanic-serving institution in the Western United States. We applied three different statistical models to predict the student’s weight status given 34 covariates such as gender, ethnicity, stress, and alcohol assumptions. The analysis based on our dataset shows that the neural network model has the highest performance. Our findings have significant implications for students’ health

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

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 AI-enabled 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

Identification of Risk Factors Associated with Obesity and Overweight - A Machine Learning Overview

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Machine Learning Approach of Obesity Level Classification: A Systematic Literature Review of Methods and Factors

The high prevalence of obesity over the years has become a global concern, as obesity contributes to an increased risk of many deadly diseases, such as diabetes, heart disease, and some cancers. This condition has become a serious concern for public health authorities, researchers, and the general public. Therefore, a comprehensive and effective approach is needed to tackle this obesity problem. Machine learning can be the answer to the required approach as it offers a method to predict the risk level of obesity through identifying the risk causes quickly and accurately. Through this approach, the most influential factors in obesity risk can be identified to aid in the development of more effective prevention and intervention strategies. Understanding the correlation between risk factors and obesity will hopefully lead to better solutions in addressing global obesity.The increasing prevalence of obesity, which amplifies the risk of diseases such as diabetes, heart disease, and cancer, has raised global concerns. Health authorities, researchers, and the community demand comprehensive solutions. Machine learning holds promise in projecting obesity risks by accurately identifying its causes. This study reviews literature from 2018-2023, focusing on factors influencing obesity and the application of machine learning in risk detection, using the SPAR-4-SLR approach for systematic guidance. The findings highlight key risk factors and machine learning methods for prediction, as well as optimization strategies for predictive models. By understanding the relationship between risk factors, obesity, and machine learning-based solutions, this research aims to identify, evaluate, and synthesize relevant literature in a specific research field to provide a comprehensive understanding of the topic

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Obesity has become a widespread problem that affects not only physical well-being but also mental health. To address this problem and provide solutions, Machine Learning (ML) technology tools are being applied. Studies are currently being developed to improve the prediction of obesity. This study aimed to predict obesity levels in nutritional patients by analyzing their physical and dietary habits using the Decision Tree (DT) model. For the development of this work, we chose to use the CRISP-DM framework to follow the development in an organized way, thus achieving a better understanding of the data and describing, evaluating, and analyzing the results. The results of this work yielded metrics with significant values for predicting obesity: so much so that the accuracy rate was 92.89%, the sensitivity rate was 94% and the F1 score was 93%. Likewise, accuracy metrics above 88% were obtained for each level of obesity, demonstrating the effectiveness of the DT model in predicting this type of task. Finally, the results demonstrate that the DT model is effective in predicting obesity, with significant results that motivate further research to continue improving accuracy in this type of task

Childhood social factors and their impact on young adulthood obesity

Obesity is a growing concern in the United States, particularly among children and adolescents. This study aimed to ascertain what factors including school/peer influences, culture, family lifestyle, and neighborhood environment, during individuals’ adolescence, impact their weight as young adults. The researcher conducted a survey that was given to students at a private catholic college in southern New England. A survey instrument was developed and distributed as a Facebook event restricted to college students at the specific institution, and additionally was distributed to one social work undergraduate course. The findings indicated that participant’s weight was most influenced by family lifestyle and school environment factors. By learning this information the researcher was able to make implications for practice, policy, and research

Essays on Health Economics Using Big Data

This dissertation consists of three essays addressing different topics in health economics. In the first essay, we perform a systematic review of peer-reviewed articles examining consumer preference for the main electronic cigarette (e-cigarette) attributes namely flavor, nicotine strength, and type. The search resulted in a pool of 12,933 articles; 66 articles met the inclusion criteria for this review. Current literature suggests consumers preferred flavored e-cigarettes, and such preference varies with age groups and smoking status. Consumer preference for nicotine strength and types depend on smoking status, e-cigarette use history, and gender. Adolescents consider flavor the most important factor trying e-cigarettes and were more likely to initiate vaping through flavored e-cigarettes. Young adults prefer sweet, menthol, and cherry flavors, while non-smokers, in particular, prefer coffee and menthol flavors. Adults in general also prefer sweet flavors (though smokers like tobacco flavor the most) and dislike flavors that elicit bitterness or harshness. Non-smokers and inexperienced e-cigarettes users tend to prefer no nicotine or low nicotine e-cigarettes while smokers and experienced e-cigarettes users prefer medium and high nicotine e-cigarettes. Weak evidence exists regarding a positive interaction between menthol flavor and nicotine strength. In the second essay, we investigate U.S. adult consumer preference for three key e-cigarette attributes––flavor, nicotine strength, and type––by applying a discrete choice model to the Nielsen scanner data (Consumer Panel data combined with retail data) for 2013 through 2017, generating novel findings as well as complementing the large literature on the topic using focus groups, surveys, and experiments. We found that (adult) vapers prefer tobacco flavor, medium nicotine strength, and disposables, and such preference can vary over cigarette smoking status, purchase frequency, gender, race, and age. In particular, smokers prefer tobacco flavor, non-smokers or female vapers prefer medium strength, and infrequent vapers prefer disposables. Vapers also display loyalty (inertia) to e-cigarette brands, flavor, and nicotine strength. One key policy implication is that a flavor ban will likely have a relatively larger impact on adolescents and young adults than adults. The third essay employs a machine learning algorithm, particularly a random forest, to identify the importance of BMI information during kindergarten on predicting children most likely to be obese by the 4th grade. We use the Arkansas BMI screening program dataset. The potential value of BMI information during early childhood to predict the likelihood of obesity later in life is one of the main benefits of a BMI screening program. This study identifies the value of this information by comparing the results of two random forests trained with and without kindergarten BMI information to assess the ability of BMI screening to improve a predictive model beyond personal, demographic, and socioeconomic measures that are typically used to identify children at high risk of excess weight gain. The BMI z-score from kindergarten is the most important variable and increases the accuracy of the prediction by 14%. The ability of BMI screening programs to identify children at greatest risk of becoming obese is an important but neglected dimension that should be used in evaluating the overall utility. In the last essay, we use Nielson retail scanner dataset and apply a difference-in-differences (DID) approach and synthetic control method, and we test whether consumers in Utah reduced beef purchases after the 2009 Salmonella outbreak of ground beef products. The result of DID approach indicates that the Salmonella event reduced ground beef purchases in Utah by 17% in four weeks after the recall. Price elasticity of demand is also estimated to be -2.04; therefore, the reduction in ground beef purchases as a result of recall is comparable to almost 8.3% increase in the price of this product. Using the synthetic control method that allows us to use all of the control states to produce synthetic Utah, we found the effect of this event minimal compared to the DID effect