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Four enhanced machine learning models were used to predict obesity in high school students by focusing on both risk and protective factors: binary logistic regression; improved decision tree (IDT); weighted k-nearest neighbor (KNN); and artificial neural network (ANN). Nine health-related behaviors from the 2015 Youth Risk Behavior Surveillance System (YRBSS) for the state of Tennessee were used as...
Outlier detection is a primary step in many data mining applications. An outlier is an abnormal individual from a population, which usually leads poor accuracy in models. Medical literatures are the most reliable resources for researchers to know the progress in their research areas and latest contributions from others. Traditional keyword search retrieves all the text data that contain the keywords...
In spite of the many efforts to encourage healthier diets, obesity continues to be a serious public health concern in the United States and across the world. Most dietary approaches rely on detailed individual tracking of points or calories, which makes the necessary long-term compliance challenging and ineffective. It has recently been shown that simply monitoring and regulating the number of bites...
Various network relationships in many complex social systems can be described effectively by multilayer networks, but we find that there are interactions between individuals attributes and their social relationships by a principle of homophily, which hence impact the process of information spread and social influence in complex social systems. In order to integrate individuals relationships and attributes...
Modeling and predicting human behaviors, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. The user diversity, dynamic behaviors, and hidden social influences make the problem more challenging. In this work, we propose a deep learning model named Social Restricted Boltzmann Machine (SRBM) for...
Human behavioral interventions aimed at improving health can benefit from objective wearable sensor data and mathematical models. Smartphone-based sensing is particularly practical for monitoring behavioral patterns because smartphones are fairly common, are carried by individuals throughout their daily lives, offer a variety of sensing modalities, and can facilitate various forms of user feedback...
Conceptual modelling, one of the first stages in a simulation study, is about understanding the situation under study and deciding what and how to model. We argue that stakeholder involvement as part of conceptual modelling could lead to a more successful simulation study with better prospects for implementation. Our work is mainly applied in health care studies, which are characterized by many stakeholders...
Studies of obesity and eating disorders need objective tools of Monitoring of Ingestive Behavior (MIB) that can detect and characterize food intake. In this paper we describe detection of food intake by a Support Vector Machine classifier trained on time history of chews and swallows. The training was performed on data collected from 18 subjects in 72 experiments involving eating and other activities...
The human body plays host to thousands of bacterial species in a variety of ecosystems. Until recently, microbial communities have been impossible to investigate thoroughly, as the vast majority of bacteria cannot be cultured through laboratory techniques. New technologies (e.g. high-throughput sequencing, 16S rRNA surveys) allow us to deeply sample the genetic content of a microbial environment in...
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