Liver disease prediction using machine learning and deep learning: A comparative study

There are various diseases associated with the human liver, some of which are hard to detect using just the information exchanged between a patient and a doctor. Motivated by the vast potential of AI in medicine, in this study, we attempted to find a model which can predict the occurrence of liver disease in a given patient with the highest accuracy, based on different input factors. A dataset was chosen to train and test this model; Indian Liver Patient Dataset obtained from UCI ML Repository. We implemented different machine learning and deep learning algorithms (Multi-Layer Perceptron, Stochastic Gradient Descent, Restricted Boltzmann Machine with Logistic Regression, Support Vector Machines, and Random Forest) and filtered out the DL-based MLP (Multi-Layer Perceptron) model as the one providing the highest Accuracy, which was compared for each model along with the Precision, Recall and fl scores. This research aims to impart insight additional to the current state-of-the-art discoveries by focusing on a comparative analysis of some of the best ML/DL techniques which haven't been scrutinized altogether yet.