Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...
Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...
ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...
OBJECTIVE: Obesity is a global health problem. The aim is to analyze the effectiveness of machine learning models in predicting obesity classes and to determine which model performs best in obesity ...
This is perhaps the most well-known dataset in pattern recognition. First introduced by Sir R.A. Fisher in 1936, it has since become a standard for testing classification algorithms. Note: This ...
ABSTRACT: Elderly individuals undergoing long-term neuroleptic therapy are increasingly vulnerable to cognitive decline, a condition that significantly impairs quality of life and increases healthcare ...
Logistic regression is a tool used in data science and machine learning to predict binary outcomes. Applications range from determining customer behaviors to diagnosing diseases. While the concept of ...
Background: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population.
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