Researchers developed and validated a machine-learning algorithm for predicting nutritional risk in patients with nasopharyngeal carcinoma.
Abstract: Decision trees in machine learning achieved satisfactory performance in classification. Decision trees offer the advantage of handling high-dimensional and complexly correlated data through ...
Abstract: Tabular data is the most prevalent form of structured data, necessitating robust models for classification and regression tasks. Traditional models like eXtreme Gradient Boosting (XGBoost) ...
ABSTRACT: Neuroleptic Malignant Syndrome (NMS) is a rare but life-threatening neurological emergency that arises primarily from the use of dopamine antagonist antipsychotic medications. Clinically, it ...
This project aims to build a multi-class text classification model for consumer complaint narratives.It categorizes complaints into four classes: Credit Reporting, Debt Collection, Consumer Loan, and ...
Background: To establish a classification model for assisting the diagnosis of type 2 diabetes mellitus (T2DM) complicated with coronary heart disease (CHD). Methods: Patients with T2DM who underwent ...
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell ...
ABSTRACT: In many fields, particularly that of health, the diagnosis of diseases is a very difficult task to carry out. Therefore, early detection of diseases using artificial intelligence tools can ...
The western Pacific seamount area is abundant in both biological and mineral resources, making it a crucial location for international investigation of regional seabed resources. An essential stage in ...
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