Abstract
The incidence of Liver disease has been steadily rising due to the excessive consumption of alcohol, exposure to harmful gases through inhalation, consumption of contaminated food, and the use of certain drugs. Dataset was used that extracted from ultrasound images for liver, and some chemical compounds (bilirubin, albumin, proteins, alkaline phosphatase) present in human body as features, to build and evaluate a classifier model, it can classify and differentiate liver diseases. This paper with the help of machine learning technique proposes diagnosing and classifies liver diseases into liver patient (abnormal) and non-liver patient (normal), then classify the liver patient into fatty liver or cirrhosis, using many supervised learning algorithms. The findings indicated that the Random Forest (RF) algorithm exhibited superior accuracy, achieving an impressive 95.12% accuracy rate. Following closely was the Decision Tree (DT) classifier, achieving a respectable accuracy of 90.24%. These results were obtained after employing a data reduction technique involving resampling. The model's performance was subsequently assessed using a 10-fold cross-validation (CV) approach, widely regarded as the optimal method for classifier evaluation. This approach leverages resampling across various folds of the dataset during multiple iterations, enhancing the classifier's ability to generalize and consequently yielding elevated accuracy when applied to unlabeled image samples.