Investigating Critical Risk Factors in Liver Cancer Prediction

02/03/2021
by   Jinpeng Li, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 5

page 6

page 7

03/17/2019

Machine Learning: A Dark Side of Cancer Computing

Cancer analysis and prediction is the utmost important research field fo...
04/07/2022

A Modified Net Reclassification Improvement Statistic

The continuous net reclassification improvement (NRI) statistic is a pop...
07/02/2020

Crowdfunding for Design Innovation: Prediction Model with Critical Factors

Online reward-based crowdfunding campaigns have emerged as an innovative...
01/12/2019

Personalized Colorectal Cancer Survivability Prediction with Machine Learning Methods

In this work, we investigate the importance of ethnicity in colorectal c...
09/21/2021

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Objective: We compared six commonly used logistic regression methods for...
06/15/2021

CatBoost model with synthetic features in application to loan risk assessment of small businesses

Loan risk for small businesses has long been a complex problem worthy of...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.