Diagnosing Liver Disease using Firefly Algorithm based on Adaboost

Liver disease is one of the most common and dangerous diseases the early detection of which can be very effective in preventing complications as well as controlling and treating the disease. The purpose of this study was to improve Adaboost algorithm using Firefly Algorithm for diagnosing liver disease.

This is a descriptive-analytic study. The dataset consists of 583 independent records including 10 features of machine learning dataset in the University of California, Irvine. In this study, Adaboost and Firefly Algorithm were combined to increase the effectiveness of liver disease diagnosis. 80% of the data were used for training and 20% for testing.

The results highlighted the superiority of the hybrid model of feature selection over the models without feature selection. Of course, the selection of important features affect the performance of the model. The accuracy of the hybrid model considering 5 and all features was 98.61% and 94.15%, respectively. Overall, the hybrid model proved more accurate compared with most of the other data mining models.

Hybrid model can be used to help physicians identify and classify healthy and unhealthy individuals; it can also be used in medical centers to enhance accuracy and speed, and reduce costs. It cannot be claimed that the hybrid model is the best model; however, it proved more accurarate.