Controlling ore dilution in underground mining is challenging. In this study, data from a Brazilian gold mine were analyzed, covering 70 chambers and 26 variables. Six key variables were identified through decision tree analysis, forming the basis of...
Mathematical biosciences and engineering : MBE
40083282
In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the prevalence of comorbidities. In Yemen, acute comorbidities further complicate the differentiation bet...
Alignment of advanced cutting-edge technologies such as Artificial Intelligence (AI) has emerged as a significant driving force to achieve greater precision and timeliness in identifying cardiovascular diseases (CVDs). However, it is difficult to ach...
BACKGROUND: Postdischarge venous thromboembolism (pdVTE) is a life-threatening complication following resection for pancreatic cancer (PC). While national guidelines recommend extended chemoprophylaxis for all, adherence is low and ranges from 1.5 to...
In this study, a decision tree derived from scientific literature on selective dry cow therapy (ST), which was developed as a knowledge base for a digital expert system, was evaluated. The decision tree merges algorithmic (based on cell count results...
Preterm birth is a significant public health concern, given its correlation with neonatal mortality and morbidity. The aetiology of preterm birth is complex and multifactorial. The objective of this study was to develop and compare machine learning m...
There is a need to increase the performance and longevity of dental composites and accelerate the translation of novel composites to the market. This study explores the use of artificial intelligence (AI), specifically machine learning (ML) models, t...
Proceedings of the National Academy of Sciences of the United States of America
39951504
Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision t...
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
39945884
PURPOSE: To develop models using different machine learning algorithms to predict high-risk symptom burden clusters in breast cancer patients undergoing chemotherapy, and to determine an optimal model.
BACKGROUND: Metabolic-bariatric surgery (MBS) is the last effective way to lose weight whom around half of the patients are women of reproductive age. It is recommended an interval of 12 months between surgery and pregnancy to optimize weight loss an...