Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

Implement Logistic Regression in Python from Scratch ! In this video, we will implement Logistic Regression in Python from Scratch. We will not use any build in models, but we will understand the code ...

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...

Abstract: Outsourcing logistic regression classification services to the cloud is highly beneficial for streaming data. However, it raises critical privacy concerns for the input data and the training ...

The aim is to build a simple classification model for the given case scenario. The problem statement and the supporting dataset are attached for your reference. After building the classification model ...

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 ...

Department of HIV and Tuberculosis Research, Ethiopian Public Health Institute, Addis Ababa, Ethiopia Background: Understanding the multifactorial drivers of female sex workers' (FSWs) engagement in ...

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: The classification problem represents a funda-mental challenge in machine learning, with logistic regression serving as a traditional yet widely utilized method across various scientific ...