Investigating Critical Risk Factors in Liver Cancer Prediction

TL;DR

This paper develops a machine learning model for liver cancer prediction using epidemiological data from over 55,000 individuals, achieving an AUC of 0.71, and identifies key risk factors influencing the model's predictions.

Contribution

It introduces a liver cancer prediction model based on large-scale epidemiological data and analyzes the most influential risk factors affecting the model's performance.

Findings

AUC of 0.71 achieved

Identified critical risk factors

Analyzed model parameters

Abstract

We exploit liver cancer prediction model using machine learning algorithms based on epidemiological data of over 55 thousand peoples from 2014 to the present. The best performance is an AUC of 0.71. We analyzed model parameters to investigate critical risk factors that contribute the most to prediction.