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Psychotropic medications: a descriptive study of prescription trends in Tabriz, Iran, 2021-2022.

BMC psychiatry
INTRODUCTION: Mental disorders, such as anxiety and depression, significantly impacted global populations in 2019 and 2020, with COVID-19 causing a surge in prevalence. They affect 13.4% of the people worldwide, and 21% of Iranians have experienced t...

Different pixel sizes of topographic data for prediction of soil salinity.

PloS one
Modeling techniques can be powerful predictors of soil salinity across various scales, ranging from local landscapes to global territories. This study was aimed to examine the accuracy of soil salinity prediction model integrating ANNs (artificial ne...

Web application using machine learning to predict cardiovascular disease and hypertension in mine workers.

Scientific reports
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 201...

Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.

Scientific reports
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and miti...

Machine learning models for water safety enhancement.

Scientific reports
Humans encounter both natural and artificial radiation sources, including cosmic rays, primordial radionuclides, and radiation generated by human activities. These radionuclides can infiltrate the human body through various pathways, potentially lead...

Predictive modeling of air quality in the Tehran megacity via deep learning techniques.

Scientific reports
Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO,...

Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS).

Endocrinology, diabetes & metabolism
INTRODUCTION: In Iran, the assessment of osteoporosis through tools like dual-energy X-ray absorptiometry poses significant challenges due to their high costs and limited availability, particularly in small cities and rural areas. Our objective was t...

Predicting Gestational Diabetes Mellitus in the first trimester using machine learning algorithms: a cross-sectional study at a hospital fertility health center in Iran.

BMC medical informatics and decision making
BACKGROUND: Gestational Diabetes Mellitus (GDM) is a common complication during pregnancy. Late diagnosis can have significant implications for both the mother and the fetus. This research aims to create an early prediction model for GDM in the first...

Improving groundwater quality predictions in semi-arid regions using ensemble learning models.

Environmental science and pollution research international
Groundwater resources constitute one of the primary sources of freshwater in semi-arid and arid climates. Monitoring the groundwater quality is an essential component of environmental management. In this study, a comprehensive comparison was conducte...

Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample.

Reproductive health
BACKGROUND: Psychologically traumatic childbirth leads to short and long-term negative impacts on a woman's health and impacts future reproductive decisions. Considering the importance of fertility growth and strengthening positive fertility motivati...