Random forest and support vector machine based hybrid liver disease detection

Tsehay Admassu Assegie (Injibara University)
Rajkumar Subhashni (St. Peter’s Institute of Higher Education and Research)
Napa Komal Kumar (St. Peter’s Institute of Higher Education and Research)
Jijendira Prasath Manivannan (HCL Technologies)
Pradeep Duraisamy (M.Kumarasamy College of Engineering)
Minychil Fentahun Engidaye (Injibara University)

Bulletin of Electrical Engineering and Informatics
Vol 11, No 3: June 2022

This study develops an automated liver disease detection system using a support vector machine and random forest detection techniques. These techniques are trained on data containing the information collected from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. The proposed system can detect the presence of liver disease in the test set. The random forest model is used for recursive feature elimination at the pre-processing stage and the support vector machine is trained on the optimal feature set. The experimental result shows that the proposed support vector machine (SVM) model has achieved 78.3% accuracy.