No abstract

No abstract

No abstract

No abstract

The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease. Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases. Among many existing methods, a few have considered the class imbalance issues of liver disorder datasets. As all the samples of liver disorder datasets are not useful, they do not contribute to learning about classifiers. A few samples might be redundant, which can increase the computational cost and affect the performance of the classifier. In this paper, a model has been proposed that combines noise filter, fuzzy sets, and boosting techniques (NFFBTs) for liver disease prediction. Firstly, the noise filter (NF) eliminates the outliers from the minority class and removes the outlier and redundant pair from the majority class. Secondly, the fuzzy set concept is applied to handle uncertainty in datasets. Thirdly, the AdaBoost boosting algorithm is trained with several learners viz, random forest (RF), support vector machine (SVM), logistic regression (LR), and naive Bayes (NB). The proposed NFFBT prediction system was applied to two datasets (i.e., ILPD and MPRLPD) and found that AdaBoost with RF yielded 90.65% and 98.95% accuracy and F1 scores of 92.09% and 99.24% over ILPD and MPRLPD datasets, respectively.