A study published in Discover Artificial Intelligence used logistic regression, random forest and support vector machine (SVM ...

Post-operative delirium (POD) is a common complication in older adults undergoing total knee arthroplasty (TKA). Procedure-specific predictive models remain limited. This study aimed to develop a ...

Logistic regression is a statistical method used to model binary outcome variables, such as whether a patient recovers or not, using a set of predictors. There are many competing methods for ...

ABSTRACT: Credit risk assessment is a fundamental component of banking operations, directly influencing lending decisions, capital allocation, pricing strategies, and regulatory compliance.

Machine learning acts as a bridge between raw data and action, helping organizations turn patterns in data into predictions, automation and more informed decisions. Different machine learning methods ...

Wilson disease (WD) is a rare autosomal recessive copper metabolism disorder, with hypersplenism as a severe, common complication secondary to disease-related cirrhosis. Currently, there is a lack of ...

Abstract: This study addresses the lack of comprehensive evaluations of feature scaling by systematically assessing 12 techniques, including less common methods such as VAST and Pareto, in 14 machine ...

ABSTRACT: Heart disease remains one of the leading causes of mortality worldwide, accounting for millions of deaths annually. Early detection of individuals at risk is essential for reducing ...

You're building a fraud detection system. Your linear regression model spits out a prediction of 1.5 for a transaction. What does 150% probability of fraud even mean? It doesn't. Linear regression can ...

This project uses a Logistic Regression machine learning model to predict whether it will rain in a given city, based on real-time weather data. The model is trained on historical weather observations ...