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A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.

BMC medical informatics and decision making
BACKGROUND: Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of mach...

Environmental contaminants in coastal populations: Comparisons with the National Health and Nutrition Examination Survey (NHANES) and resident dolphins.

The Science of the total environment
BACKGROUND: People living in coastal communities are at risk for exposure to environmental hazards, including legacy chemicals. We can use databases such as NHANES to assess whether contaminants in coastal communities are present in higher levels tha...

Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity.

Scientific reports
Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activi...

Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm.

Journal of affective disorders
BACKGROUND: As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depressio...

A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models.

IEEE journal of biomedical and health informatics
We consider the problem in precision health of grouping people into subpopulations based on their degree of vulnerability to a risk factor. These subpopulations cannot be discovered with traditional clustering techniques because their quality is eval...

A review of machine learning in obesity.

Obesity reviews : an official journal of the International Association for the Study of Obesity
Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning prov...

Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis.

Journal of healthcare engineering
BACKGROUND: Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy.