GitHub

Deepak-Lingala/Customer-Churn-Prediction

A telecommunications company loses roughly 20% of its customers monthly but struggles to identify at-risk profiles from 7k+ messy customer records spread across billing and usage tables.
IEEE

Establishing Ensemble Boosting Algorithm Based Models with SMOTE to Forecast the Initial Vulnerability of Gastric Cancer (GC) with Patient Dataset

Abstract: Gastric cancer (GC) remains a significant global health concern, exhibiting persistently high mortality rates predominantly attributable to late-stage clinical diagnosis. Prognostic and ...
GitHub

HetGSMOTE: Oversampling for Heterogeneous Graphs

Graph Neural Networks (GNNs) have proven effective for learning from graph structured data, with heterogeneous graphs (HetGs) gaining particular prominence for their ability to model diverse real ...
IEEE

Addressing Class Imbalance in Medical Data: A Synthetic Minority Oversampling Technique (SMOTE) Approach for Chronic Kidney Disease Prediction

Abstract: Chronic Kidney Disease (CKD) is a significant health concern in contemporary society. Due to its early asymptomatic nature, it is frequently referred to as the silent sickness. Consequently, ...