• Deepak Kumar

Liver cirrhosis is a serious condition requiring early detection for effective treatment. This review explores the use of Random Forest (RF), a machine learning method, in detecting liver cirrhosis. RF models have shown high accuracy, robustness to missing data, and strong feature selection capabilities. We summarize key studies, datasets, evaluation metrics, and compare RF with other algorithms. The paper also highlights challenges such as data limitations and clinical integration, offering insights into future research directions for improving cirrhosis detection using RF techniques.

Tuberculosis (TB), Random Forest, Machine Learning, Medical Diagnosis, Predictive Modeling, Classification

"Liver Cirrhosis detection using Random forest", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.g444-g447, July-2025, Available :http://www.jetir.org/papers/JETIR2507664.pdf

"Liver Cirrhosis detection using Random forest", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppg444-g447, July-2025, Available at : http://www.jetir.org/papers/JETIR2507664.pdf