Background Artificial intelligence ECG (AI-ECG) models can predict cardiovascular outcomes, but their clinical adoption is limited by restricted access to training data and uncertain generalisability.

Quadratic regression extends linear regression by adding squared terms and pairwise interaction terms, enabling the model to capture non-linear structure and predictor interactions. The article ...

Evaluates 5 methods (Linear Regression, KNN, Mean/Median Imputation, List-wise Deletion, Hot Deck) for imputing missing data in C. Identifies best method for 3 datasets, analyzing strengths and ...

Discrete, Gaussian, and Heterogenous HMM models full implemented in Python. Missing data, Model Selection Criteria (AIC/BIC), and Semi-Supervised training supported. Easily extendable with other types ...

A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the price of a house based on its square footage, age, number of bedrooms and ...

Shortage of beds, staff force Michigan kids out of state for mental health treatment Michigan kids with serious mental health needs are being sent thousands of miles away from their families to ...

Abstract: Missing data is one of the unavoidable issues in Wireless Sensor Networks (WSNs) due to various reasons, including communication failure, unreliable communication links, unexpected damage, ...

ABSTRACT: In this study, we investigate the effects of missing data when estimating HIV/TB co-infection. We revisit the concept of missing data and examine three available approaches for dealing with ...