AIMC Topic: Iran
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Spatial prediction of human brucellosis susceptibility using an explainable optimized adaptive neuro fuzzy inference system.
Acta tropica
Nov 30, 2024
Brucellosis, a zoonotic disease caused by Brucella bacteria, poses significant risks to human, livestock, and wildlife health, alongside economic losses from livestock morbidity and mortality. This study improves Human Brucellosis Susceptibility Mapp...
Assessing the performance of machine learning algorithms for analyzing land use changes in the Hyrcanian forests of Iran.
Environmental science and pollution research international
Nov 30, 2024
Land use changes are of critical importance in understanding and managing environmental sustainability and resource utilization. Machine learning algorithms (MLAs) have emerged as powerful tools for analyzing and predicting land use changes, offering...
Hearing loss prediction equation for Iranian truck drivers using neural network algorithm.
Work (Reading, Mass.)
Nov 25, 2024
BACKGROUND:  Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly.
Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models.
BMC psychiatry
Nov 21, 2024
BACKGROUND: Suicidal behaviors, which may lead to death (suicide) or survival (suicide attempt), are influenced by various factors. Identifying the specific risk factors for suicidal behavior mortality is critical for improving prevention strategies ...
Fuzzy multi-objective optimization for sustainable agricultural water management of irrigation networks.
Journal of environmental management
Nov 20, 2024
Sustainable water resource management in arid and water deficit regions requires optimal use of water resources due to competition among different water sectors. The purpose of this study is to model uncertainties in economic and hydro-climatic varia...
Explainable machine learning versus known nomogram for predicting non-sentinel lymph node metastases in breast cancer patients: A comparative study.
Computers in biology and medicine
Nov 16, 2024
INTRODUCTION: Axillary lymph node dissection (ALND) is the standard of care for breast cancer patients with positive sentinel lymph nodes (SLN), which are the first lymph nodes that drain the breast. However, many patients with positive SLNs may not ...
An interpretable (explainable) model based on machine learning and SHAP interpretation technique for mapping wind erosion hazard.
Environmental science and pollution research international
Nov 15, 2024
Soil erosion by wind poses a significant threat to various regions across the globe, such as drylands in the Middle East and Iran. Wind erosion hazard maps can assist in identifying the regions of highest wind erosion risk and are a valuable tool for...
Prediction of 12-month recurrence of pancreatic cancer using machine learning and prognostic factors.
BMC medical informatics and decision making
Nov 14, 2024
BACKGROUND AND AIM: Pancreatic cancer is lethal and prevalent among other cancer types. The recurrence of this tumor is high, especially in patients who did not receive adjuvant therapies. Early prediction of PC recurrence has a significant role in e...
Predicting stroke severity of patients using interpretable machine learning algorithms.
European journal of medical research
Nov 14, 2024
BACKGROUND: Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke se...
Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.
Archives of Iranian medicine
Oct 1, 2024
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. T...
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