Hepatocellular Carcinoma Detection Using Machine Learning Techniques

Hepatocellular carcinoma (HCC) is a form of primary cancer appearing in the liver. In this work used the hepatocellular carcinoma data-set from the UCI machine learning repository and tested different techniques for feature selection and classification. The following algorithms were used: decision trees, random forests, SVMs, k-NN classifiers, AdaBoost, and gradient boost. The best results were obtained using gradient boost with 84% accuracy and 93% precision. Finally, we deployed the model to a web application as a decision support system for clinicians.