Abstract: Logistic regression is a fundamental and widely used statistical method for modeling binary outcomes based on covariates. However, the presence of missing data, particularly in settings ...
A comprehensive machine learning web application that predicts disease risk based on patient medical parameters using Logistic Regression and Random Forest algorithms. This project provides an ...
Purpose: To develop a machine learning model to predict anatomical response to anti-VEGF therapy in patients with diabetic macular edema (DME). Methods: This retrospective study included patients with ...
In Table 3, the VIF values for each variable are < 5, which has been reduced as multicollinearity between variables. 3.3. Use the Entropy Weight Method to Weight the Data When exploring the factors ...
Abstract: In bioinformatics, the rapid development of sequencing technology has enabled us to collect an increasing amount of omics data. Classification based on omics data is one of the central ...
Objective: This study aimed to develop and validate a multivariate logistic regression model for predicting intracranial aneurysm (IA) rupture by integrating clinical data, aneurysm morphology, and ...
Chickenpox is a common acute and highly contagious disease in childhood; moreover, there is currently no targeted treatment. Carrying out an early warning on chickenpox plays an important role in ...
Gov. Gavin Newsom signs SB 243, the first US law setting child-safety rules for AI chatbots, from crisis redirects to transparency requirements. Hollywood pushes back against OpenAI’s Sora 2 as ...
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