AIMC Topic: Factor Analysis, Statistical

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Exploring gene-gene interaction in family-based data with an unsupervised machine learning method: EPISFA.

Genetic epidemiology
Gene-gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single-nucleotide polymorphisms. The current tools for exploring G × G were oft...

A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure.

PloS one
We created a facet atlas that maps the interrelations between facet scales from 13 hierarchical personality inventories to provide a practically useful, transtheoretical description of lower-level personality traits. We generated this atlas by estima...

Future possibilities for artificial intelligence in the practical management of hypertension.

Hypertension research : official journal of the Japanese Society of Hypertension
The use of artificial intelligence in numerous prediction and classification tasks, including clinical research and healthcare management, is becoming increasingly more common. This review describes the current status and a future possibility for art...

Identifying the Symptom Severity in Obsessive-Compulsive Disorder for Classification and Prediction: An Artificial Neural Network Approach.

Behavioural neurology
The present study is aimed at identifying the most prominent determinants of OCD along with their strength to classify the OCD patients from healthy controls. The data for this cross-sectional study were collected from 200 diagnosed OCD patients and ...

Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach.

Diabetes & metabolic syndrome
BACKGROUND AND AIMS: Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing countries including Bangladesh. The main objective of this study is to use machine learning (ML) based c...

One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis.

Psychological methods
Determining the number of factors is one of the most crucial decisions a researcher has to face when conducting an exploratory factor analysis. As no common factor retention criterion can be seen as generally superior, a new approach is proposed-comb...

Stress among Portuguese Medical Students: the EuStress Solution.

Journal of medical systems
There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this pa...

Development and validation of a questionnaire to assess public receptivity toward autonomous vehicles and its relation with the traffic safety climate in China.

Accident; analysis and prevention
The advent of autonomous vehicles (AVs) has gained increasing attention in China. Although auto manufacturers and innovators have attempted to confirm that AVs are safe and have introduced them on public roads, it is vital to understand end-users' ac...

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals.

PloS one
Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free B...

Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts.

Ergonomics
Human Factors Analysis and Classification System (HFACS) is based upon Reason's organizational model of human error which suggests that there is a 'one to many' mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens...