Overview: Predictive models turn historical data into reliable forecasts that support accurate planning across ...

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 ...

Introduction: This study aimed to develop a diabetic retinopathy (DR) Prediction model using various machine learning algorithms incorporating the novel predictor Triglyceride-glucose index (TyG).

Chad Beam provides the ins and outs of the implementation of GIS, advanced communication systems and predictive modeling to ensure that staffing, to whatever extent, is utilized most effectively. A ...

This set of notebooks enables the analysis of comorbidities associated with male infertility using structured EHR data. First, we identified nonoverlapping patients with male infertility and patients ...

Abstract: 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 ...

Objective: In this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP ...

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 ...

ABSTRACT: Heart disease continues to be a major global cause of death, making the development of reliable prediction models necessary to enable early detection and treatment. Using machine learning to ...