
ANALYZING DATA MINING TECHNIQUES FOR LIVER DISEASE PREDICTION ON IMBALANCED DATASETS
RIZWAN ARBAIN , ASIA PACIFIC UNIVERSITY OF TECHNOLOGY & INNOVATION, KUALA LUMPUR, MALAYSIAAbstract
Liver disease prediction is a critical task in healthcare, where early diagnosis can significantly improve patient outcomes. However, the imbalanced nature of medical datasets, with a disproportionate number of healthy cases compared to diseased ones, poses a challenge for predictive modeling. This study systematically analyzes and compares the performance of various data mining techniques for liver disease prediction on imbalanced datasets. We employ a comprehensive set of evaluation metrics to assess the algorithms' effectiveness in handling imbalanced data and achieving accurate predictions. The results provide insights into the choice of algorithms and preprocessing techniques that enhance the reliability of liver disease prediction models.
Keywords
Data Mining, Liver Disease Prediction, Imbalanced Datasets, Predictive Modeling, Healthcare
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