Computative Analysis of Various Techniques for Classification of Liver Disease

This paper presents a software-engineered approach using a classification algorithm for the classification of liver disease. The ILPD dataset is used for the proposed work. Different attributes of liver patient records such as direct bilirubin, age, sex, total bilirubin, alphos, albumin, sgpt, globulin ratio, sgot are used to classify liver disease. The proposed Convolution Neural Network classification technique shows an accuracy of 67% and a precision of 71%. Various classification algorithms such as CNN, RNN, ANN, and logistic regression are executed on the liver patient dataset and their accuracy is determined.