Scientific Research Publishing

Hosmer, D.W. and Lemeshow, S. (2000). Applied Logistic Regression. 2nd Edition, Wiley.

ABSTRACT: Antidepressant-induced mania represents a significant clinical challenge in the management of bipolar depression, with incidence rates ranging from 5% - 20% in clinical populations. Despite ...
PNAS

Comparing variable selection and model averaging methods for logistic regression

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 ...
BMJ

Prediction of Oswestry Disability Index and Numeric Rating Scale scores after lumbar spine surgery: machine learning model development and fairness assessment

Background One-third of patients operated for degenerative conditions in the lumbar spine do not report substantial improvement after 12 months. Most previous outcome prediction models are classifiers ...
Hosted on MSN

Linear regression using gradient descent explained simply

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...
IEEE

Fuzzy LASSO Logistic Regression: A Regularized Framework for Interpretable Binary Classification Under Uncertainty

Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...
Nature

Integrating multi-omics and clinical features to model survival in epithelial ovarian cancer subtypes

Epithelial ovarian cancer (EOC) exhibits significant heterogeneity in clinical outcomes, influenced by histology, age, stage, and molecular characteristics. This study aimed to develop and validate a ...
GEN

Machine-Learning-Assisted mAb Yield Improvement

Monoclonal antibody (mAb) manufacturing must continually improve to keep up with increasing demands. To do this, biomanufacturers can deploy machine learning tools to augment traditional process ...
Scientific Research Publishing

Heart Disease Prediction: A Logistic Regression Approach ()

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 ...
GitHub

pH-7/Python-GLM-ML-Pipeline

. ├── app/ # FastAPI application ├── train/ # Training scripts ├── assets/images/ # Images, diagrams ├── requirements.txt # Python dependencies ├── Dockerfile ├── .env.dist # Sample environment ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
IEEE

Secure and Efficient Logistic Regression With Secret-Sharing MPC and Differential Privacy

Abstract: Machine learning models are being increasingly deployed in sensitive applications where data privacy and model security are of paramount importance. This paper introduces a novel ...
Nature

Predicting postoperative trauma-induced coagulopathy in patients with severe injuries by machine learning

Trauma-induced coagulopathy (TIC) has a high incidence in patients with severe trauma. Patients who develop TIC usually have a poor prognosis, characterised by increased organ dysfunction, ...