Liver Disease Detection using Machine Learning

Author(s)	K.Swapna, CH.Spoorthi, K.Sirireddy, K.Sowmya, K.Harshitha
Country	India
Abstract	Liver disease is a major global health concern causing high mortality rates. Early detection plays a crucial role in improving treatment outcomes and reducing deaths. This research proposes a machine learning–based system for predicting liver disease using clinical data. The model utilizes the Random Forest algorithm for classification and applies the SMOTE + ENN technique to handle class imbalance in medical datasets. Performance is evaluated using accuracy, precision, recall, and F1-score. The proposed system significantly improves prediction accuracy and reduces false classifications compared to traditional approaches.
Keywords	Liver Disease, Machine Learning, Random Forest, SMOTE-ENN, Classification.
Field	Engineering
Published In	Volume 17, Issue 1, January-June 2026
Published On	2026-04-12
Cite This	Liver Disease Detection using Machine Learning - K.Swapna, CH.Spoorthi, K.Sirireddy, K.Sowmya, K.Harshitha - IJAIDR Volume 17, Issue 1, January-June 2026.

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Journal of Advances in Developmental Research