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 ...

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 ...

This video is an overall package to understand L2 Regularization Neural Network and then implement it in Python from scratch. L2 Regularization neural network it a technique to overcome overfitting.

Logistic regression is the most cost-effective model for medial vascular calcification classification, with a mean ICER of $278 using five low-cost features. Despite similar diagnostic accuracy, ...

Learn how to implement Logistic Regression from scratch in Python with this simple, easy-to-follow guide! Perfect for beginners, this tutorial covers every step of the process and helps you understand ...

A project at Rice University has developed a new machine learning (ML) algorithm intended to improve the identification of biomarkers in optical spectra. As reported in ACS Nano, the algorithm could ...

This is a classification model implementation using Random Forest and Logistic Regression in Python and Spark. Originally implemented via AWS EMR Clusters.

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.