Improved Modeling of Imbalanced Data for Dietary Lapse Prediction

• Activity Number: 676 - Analysis and Reporting: Benefit-Risk and Robust Models
• Type: Contributed
• Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Section on Medical Devices and Diagnostics
• Author(s): Fengqing Zhang* and Tinashe M Tapera and Stephanie P Goldstein and Evan Forman
• Companies: Drexel University and Drexel University and Drexel University and Drexel University
• Keywords: Ecological Momentary Assessment; Imbalanced Data; Classification; Variable Selection

Abstract

Obesity, a condition present in 35% of US adults, increases risk for numerous diseases, and reduces quality of life. Participants in weight loss programs struggle to remain adherent to a dietary prescription. Specific moments of inadherence, known as dietary lapses, are the cause of weight control failure. We developed a smartphone app that utilizes just-in-time adaptive intervention and machine learning to predict and prevent dietary lapses. Users were repeatedly prompted to enter information about lapses and a set of potentially triggering factors (e.g., mood) using a repeated sampling method called ecological momentary assessment. The resulting data have an unbalanced ratio of lapses to non-lapses approximately 1:12. Classification of data with imbalanced class distribution is challenging. To this end, we developed a cost-sensitive ensemble model as a meta-technique to combine multiple weak classifiers and introduce cost items into the learning framework. We also designed a neighborhood based balancing strategy to redefine the training set for a given test set. Results showed that the proposed model works efficiently and effectively for lapse prediction.