Prediction of Incident Cancers in the Lifelines Population-Based Cohort

Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer w...

Full description

Saved in:
Bibliographic Details
Published in:Cancers 2021-04, Vol.13 (9), p.2133
Main Authors: Cortés-Ibañez, Francisco O, Nagaraj, Sunil Belur, Cornelissen, Ludo, Navis, Gerjan J, van der Vegt, Bert, Sidorenkov, Grigory, de Bock, Geertruida H
Format: Article
Language:English
Subjects:
Age
Algorithms
Bias
Breast cancer
Classification
Datasets
Diagnosis
Learning algorithms
Lifestyles
Machine learning
Medical diagnosis
Medical prognosis
Medical screening
Population
Predictions
Prostate cancer
Questionnaires
Risk factors
Secondary education
Skin cancer
Socioeconomic factors
Support vector machines
Vocational education
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC)
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers13092133