What type of study is this?

This is a Experimental Study study.

September 18, 2025Deleted Journal

Class-Imbalance Aware Machine Learning for CKD Detection and Risk Assessment

View Full Paper

Ask AI

Bookmark

Authors

This analysis implements a Random Forest classifier to improve chronic kidney disease identification, highlighting class imbalance implications.

The machine learning model achieved 100% sensitivity for CKD detection, indicating high effectiveness in identifying cases.
Key predictors of CKD included Serum Creatinine and Glomerular Filtration Rate, supporting established medical knowledge.
Class imbalance negatively impacted specificity, necessitating methods like class weighting to refine model accuracy.
Machine learning offers promising decision support for CKD management, yet challenges with data imbalance and generalizability persist.

Ashish Kumar (2024) studied this question.

View Full Paper

Ask AI

Bookmark

September 18, 2025Deleted Journal

View Full Paper

Ask AI

Bookmark

Authors

This analysis implements a Random Forest classifier to improve chronic kidney disease identification, highlighting class imbalance implications.

The machine learning model achieved 100% sensitivity for CKD detection, indicating high effectiveness in identifying cases.
Key predictors of CKD included Serum Creatinine and Glomerular Filtration Rate, supporting established medical knowledge.
Class imbalance negatively impacted specificity, necessitating methods like class weighting to refine model accuracy.
Machine learning offers promising decision support for CKD management, yet challenges with data imbalance and generalizability persist.

Ashish Kumar (2024) studied this question.

View Full Paper

Ask AI

Bookmark