Liver disease classification using histogram-based gradient boosting classification tree with feature selection algorithm

Abstract

Healthcare is the key for everyone to run daily life, and health diagnosing techniques should be accessible easily. Indeed, the early identification of liver disease will be supportive for physicians to make decisions. Utilizing feature selection and classification approaches, this work aims to predict liver disorders through machine learning. The Histogram-based Gradient Boosting Classification Tree with a recursive feature selection algorithm (HGBoost) is proposed in this paper. The recursive feature selection approach and the Gradient Boosting are used to forecast liver disease. Using data from Indian liver patient records, the proposed HGBoost method has been assessed. Assessing the accuracy, confusion matrix, and area under curve involves implementing and comparing a variety of classification techniques, including MLP, Gboost, Adaboost, and proposed HGBoost algorithms. With the help of the recursive feature selection technique, the proposed HGBoost has surpassed other current algorithms. In comparison to the MLP, RF, Gboost, Adaboost, and proposed HGBoost algorithms, the enhanced accuracy is between 4 and 9% and between 1 and 7 % of the MSE error.