Python R, NUMPY, Machine Learning, Predictive Analytics, Tableau, Power BI, Qlikview, Forecasting, Linear Regression, Logistic Regression, Decision Trees, CHAID, CART ...

Lung cancer remains one of the most important issues related to cancer mortality worldwide due to the late diagnosis and the need for invasive biopsy procedures. There has emerged a new and less ...

About Predicting bank customer churn with logistic regression. Data cleaning, modeling, and evaluation using Python and scikit-learn.

Context: Attempting to use the SHAP library for model interpretation after training and selecting the best model (Logistic Regression). Observed Error: ImportError: Numba needs NumPy 2.2 or less. G ...

Multinomial logistic regression was used to model discharge disposition, and logistic regression was used for 30-day readmission. Forward stepwise regression based on the Akaike information criterion ...

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 ...

Important features like mutation and variant allele frequencies and methylation profiles will be used as input parameters in constructing the predictive model. This work intends to focus on the ...

Recently developed models, such as the Age-NIHSS–American Society of Intervention and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) (ANA) score and nomograms based on ...

Abstract Purpose To aid personalized treatment selection, we developed a predictive model for acute rectal toxicity in patients with prostate cancer undergoing radiotherapy with photons and protons.

Abstract The accurate and early detection of coronary heart disease (CHD) is crucial for reducing mortality rates. This study evaluates the predictive performance of three machine learning ...