Causal discovery approach with reinforcement learning for risk factors of type II diabetes mellitus.

Background: Statistical correlation analysis is currently the most typically used approach for investigating the risk factors of type 2 diabetes mellitus (T2DM). However, this approach does not readily reveal the causal relationships between risk factors and rarely describes the causal relationships visually.

Results: Considering the superiority of reinforcement learning in prediction, a causal discovery approach with reinforcement learning for T2DM risk factors is proposed herein.

Improved Functional Causal Likelihood-Based Causal Discovery Method for Diabetes Risk Factors.

Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors.

Inhibitory Effect of Piceatannol on Infection Both and .

Suilysin (SLY) plays a critical role in infections making it an ideal target to the combat infection caused by this pathogen. In the present study, we found that piceatannol (PN), a natural compound, inhibits pore-formation by blocking the oligomerization of SLY without affecting the growth of and the expression of SLY. Furthermore, PN alleviated the J774 cell damage and the expression of the inflammatory cytokine tumor necrosis factor-α (TNF-α) and interleukin-1α (IL-1β) induced by .

Predicting human body composition using a modified adaptive genetic algorithm with a novel selection operator.

Background: Changes to human body composition reflect changes in health status to some extent. It has been recognized that these changes occur earlier than diseases. This means that a reasonable prediction of body composition helps to improve model users' health.

The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine.

: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems.

A human body physiological feature selection algorithm based on filtering and improved clustering.

Research: The body composition model is closely related to the physiological characteristics of the human body. At the same time there can be a large number of physiological characteristics, many of which may be redundant or irrelevant. In existing human physiological feature selection algorithms, it is difficult to overcome the impact that redundancy and irrelevancy may have on human body composition modeling.