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

Heart Disease Prediction Using XGBoost: Model Development, Evaluation, and Interpretability

This repository contains a Python script that builds, trains, and evaluates a machine learning model using the XGBoost classifier. The model is designed to predict the target column based on the other ...
Frontiers

Predicting risk of obesity in overweight adults using interpretable machine learning algorithms

This cross-sectional study was conducted between June 2011 and January 2012. The participants were randomly selected using a simple random sampling technique. Seven commonly used machine learning ...
IEEE

Employee Attrition Prediction Using Classification Models

Abstract: The term Attrition refers to the voluntary or involuntary discontinuation of employees in an organization. This paper focuses on discussing a systematic flow for predicting Attrition using ...
GitHub

scores: Verification and Evaluation for Forecasts and Models

A list of over 75 metrics, statistical techniques and data processing tools contained in scores is available here. scores is a Python package containing mathematical functions for the verification, ...
IEEE

Heart Disease Prediction using Ensemble Learning

Abstract: Early disease prediction prevents patients to encounter severe situations. Transition from symptoms to disease stage of any disease takes time. Prediction of disease in this transition stage ...
Frontiers

A predictive model based on random forest for shoulder-hand syndrome

We retrospectively studied all the first-onset stroke patients with one-side hemiplegia, then 36 patients that met the criteria were included. The patients’ data concerning a wide spectrum of ...