A Robust Deep Learning based Framework for High-Precision Detection of Liver Disease

Globally, chronic liver disease is a significant cause of death, affecting a large number of people. The liver can be damaged by several factors. Obesity, undiagnosed hepatitis, and alcohol abuse, to name a few examples. This is the cause of inappropriate nerve function, blood in the cough or vomit, renal failure, liver failure, jaundice, liver encephalopathy, and many other symptoms. We use the UCI machine learning Indian Liver Patient dataset, which has 583 samples and 11 characteristics. Deep learning techniques are used to detect sickness early. Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) were the three methods used in this paper. Different measurement approaches, such as accuracy, precision, recall and f-1 score, false positive rate, negative rate, mean error etc. were used to check the performance of different techniques. In terms of accuracy, CNN, ANN, LSTM were found to be 96.58%, 95.72% and 99.23% accurate in each of these categories. We also used SVM to see the effectiveness of machine learning in this prediction and our accuracy for this was 86.23%. According to the research, the LSTM had the highest accuracy. By analyzing clinical data, we also explored other ways to display this information.