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Prevalence

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Design and Evaluation of a Postpartum Depression Ontology.

Applied clinical informatics
OBJECTIVE: Postpartum depression (PPD) remains an understudied research area despite its high prevalence. The goal of this study is to develop an ontology to aid in the identification of patients with PPD and to enable future analyses with electronic...

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.

PloS one
BACKGROUND AND OBJECTIVE: Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population's health but also affects economic and social development. It requires an accurate prediction analysis to help to m...

Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES.

Scientific reports
The prevalence of cardiocerebrovascular disease (CVD) is continuously increasing, and it is the leading cause of human death. Since it is difficult for physicians to screen thousands of people, high-accuracy and interpretable methods need to be prese...

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia.

Computational and mathematical methods in medicine
This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in ...

Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms.

Journal of health, population, and nutrition
BACKGROUND: Stress-related mental health problems are one of the most common causes of the burden in university students worldwide. Many studies have been conducted to predict the prevalence of stress among university students, however most of these ...

Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition.

Scientific reports
In this study, we proposed a new data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), cal...

Perception of yips among professional Japanese golfers: perspectives from a network modelled approach.

Scientific reports
'Yips' in golf is a complex spectrum of anxiety and movement-disorder that affects competitive sporting performance. With unclear etiology and high prevalence documented in western literature, the perception and management of this psycho-neuromuscula...

Efficient and targeted COVID-19 border testing via reinforcement learning.

Nature
Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of ad hoc border control protocols to allow for non-essential travel while safeguarding public health, from quarantining all travellers to restricting ent...

A Machine Learning Model for Evaluating Imported Disease Screening Strategies in Immigrant Populations.

The American journal of tropical medicine and hygiene
Given the high prevalence of imported diseases in immigrant populations, it has postulated the need to establish screening programs that allow their early diagnosis and treatment. We present a mathematical model based on machine learning methodologie...

Multivariate random forest prediction of poverty and malnutrition prevalence.

PloS one
Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies' programming. However, state of the art models ofte...