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Obesity and Environmental Factors; A Machine Learning Approach
- 1. Obesity and Environmental Factors A MACHINE LEARNING APPROACH Ballard, Harnagel, Peterson
- 2. Obesity is a Serious Problem Obesity is weight that is higher than what is considered as a healthy weight for a given height Nearly 40% of adults (approximately 94 million) Nearly 20% of children (approximately 13 million) Remarkably, according to the USDA, it is getting worse— quickly USDA studies indicate that by 2030 nearly one-half of the U.S. population will be obese Obesity is linked with undesirable medical issues, including: Heart disease Diabetes Cancer The costs are also staggering with estimates of $147 billion per year
- 3. Research Goals Determine if machine learning (ML) could be used to predict the obesity of an out-of-sample observation. If ML works, develop the most performant model(s): Most performant single model Most performant ensemble Interpret the relationship between (features) environmental factors and obesity (target) The astute amongst you have already identified a dilemma: Generally the most performant models are the least interpretable We pursued a dual modeling approach
- 4. The Data Source: United States Department of Agriculture (USDA) Geo-centric, each observation is a state and county combination. Wide variety of features from a broad spectrum of perspectives: access and proximity to grocery stores restaurant availability and expenditures food assistance food prices and taxes health and physical activity socioeconomic characteristics Wrangling: Removed obvious sources of data leakage Removed rows & columns missing over 90% of data Missing values imputed with median Few true strings. Coerced some features into numeric dtypes in pandas.
- 5. Interpretation Research goal was to understand relationship between features and target There is no one perfect method Fisher score was selected due to broad usage and general good performance
- 6. Performance Research goal was to develop most performant models: Single model Ensemble LogicPlum leveraged to focus on the findings
- 7. Performance Research goal was to develop most performant models: Single model Ensemble LogicPlum leveraged to focus on the findings
- 8. Conclusions ML METHODS ARE POWERFUL IN PREDICTING OBESITY ML METHODS PROVIDE INSIGHT INTO CORRELATIONS