AIMC Topic: Young Adult

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Development of a machine learning-based multivariable prediction model for the naturalistic course of generalized anxiety disorder.

Journal of anxiety disorders
BACKGROUND: Generalized Anxiety Disorder (GAD) is a chronic condition. Enabling the prediction of individual trajectories would facilitate tailored management approaches for these individuals. This study used machine learning techniques to predict th...

Age group classification based on optical measurement of brain pulsation using machine learning.

Scientific reports
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monito...

Detecting noncredible symptomology in ADHD evaluations using machine learning.

Journal of clinical and experimental neuropsychology
INTRODUCTION: Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are becoming increasingly complicated by the number of adults who fabricate or exaggerate symptoms. Novel methods are needed to improve the assessment process re...

Forensic sex classification by convolutional neural network approach by VGG16 model: accuracy, precision and sensitivity.

International journal of legal medicine
INTRODUCTION: In the reconstructive phase of medico-legal human identification, the sex estimation is crucial in the reconstruction of the biological profile and can be applied both in identifying victims of mass disasters and in the autopsy room. Du...

Machine learning-based algorithm of drug-resistant prediction in newly diagnosed patients with temporal lobe epilepsy.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVES: To develop a predicted algorithm for drug-resistant epilepsy (DRE) in newly diagnosed temporal lobe epilepsy (TLE) patients.

Research of orthodontic soft tissue profile prediction based on conditional generative adversarial networks.

Journal of dentistry
OBJECTIVE: This study constructed a new conditional generative adversarial network (CGAN) model to predict changes in lateral appearance following orthodontic treatment.

Predicting home delivery and identifying its determinants among women aged 15-49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016-2023: a machine learning algorithm.

BMC public health
BACKGROUND: Birth-related mortality is significantly increased by home births without skilled medical assistance during delivery, presenting a major risk to the public's health. The objective of this study is to predict home delivery and identify the...

Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.

PloS one
BACKGROUND: Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the availa...

Exploring the association between personality traits and colour saturation preference using machine learning.

Acta psychologica
Both personality traits and colour saturation are associated with emotion; however, how colour saturation preference interacts with different traits and whether this interaction is modulated by object-colour relations remains unclear. In this study, ...

Enhancing trauma triage in low-resource settings using machine learning: a performance comparison with the Kampala Trauma Score.

BMC emergency medicine
BACKGROUND: Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these s...