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Spatial prediction of human brucellosis susceptibility using an explainable optimized adaptive neuro fuzzy inference system.

Acta tropica
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
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...

Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models.

BMC psychiatry
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
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
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 ...

Predicting Early recurrence of atrial fibrilation post-catheter ablation using machine learning techniques.

BMC cardiovascular disorders
BACKGROUND: Catheter ablation is a common treatment for atrial fibrillation (AF), but recurrence rates remain variable. Predicting the success of catheter ablation is crucial for patient selection and management. This research seeks to create a machi...

Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters.

BMC anesthesiology
BACKGROUND: To protect patients during anesthesia, difficult airway management is a serious issue that needs to be carefully planned for and carried out. Machine learning prediction tools have recently become increasingly common in medicine, frequent...

Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran.

Environmental science and pollution research international
This study aimed to integrate game theory and deep learning algorithms with the InVEST Ecosystem Services Model (IESM) for Sediment Retention (SR) modeling in the Kasilian watershed, Iran. The Kasilian watershed is characterized by multiple sub-water...

Enhancing groundwater quality prediction through ensemble machine learning techniques.

Environmental monitoring and assessment
Groundwater quality is assessed by conducting water sampling and laboratory analysis. Field-based measurements are costly and time-consuming. This study introduces a machine learning (ML)-based framework and innovative application of stacking ensembl...

Application of machine learning in breast cancer survival prediction using a multimethod approach.

Scientific reports
Breast cancer is one of the most prevalent cancers with an increasing trend in both incidence and mortality rates in Iran. Survival analysis is a pivotal measure in setting appropriate care plans.  To the best of our knowledge, this study is pioneeri...