• Department of Physics, Florida Atlantic University, Boca Raton, FL, USA.
• Department of Therapeutic Radiology, School of Medicine, Yale University, New Haven, CT, USA.

BACKGROUND: Liver cancer is one of the most common types of cancer and the third leading cause of cancer-related deaths globally. The most common type of primary liver cancer is called hepatocellular carcinoma (HCC) which accounts for 75-85% of cases. HCC is a malignant disease with aggressive progression and limited therapeutic options. While the exact cause of liver cancer is not known, habits/lifestyles may increase the risk of developing the disease.

MATERIAL AND METHODS: This study is designed to quantify the liver cancer risk through a multi-parameterized artificial neural network (ANN) based on basic health data including habits/lifestyles. In addition to input and output layers, our ANN model has three hidden layers having 12, 13, and 14 neurons, respectively. We have used the health data from the National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) datasets to train and test our ANN model.

RESULTS: We have found the best performance of the ANN model with an area under the receiver operating characteristic curve of 0.80 and 0.81 for training and testing cohorts, respectively.

CONCLUSION: Our results demonstrate a method that can predict liver cancer risk with basic health data and habits/lifestyles. This novel method could be beneficial to high-risk populations by enabling early detection.