Personal Health Information Inference Using Machine Learning on RNA Expression Data from Patients With Cancer: Algorithm Validation Study.

ABSTRACT:

Background

As the need for sharing genomic data grows, privacy issues and concerns, such as the ethics surrounding data sharing and disclosure of personal information, are raised.

Objective

The main purpose of this study was to verify whether genomic data is sufficient to predict a patient's personal information.

Methods

RNA expression data and matched patient personal information were collected from 9538 patients in The Cancer Genome Atlas program. Five personal information variables (age, gender, race, cancer type, and cancer stage) were recorded for each patient. Four different machine learning algorithms (support vector machine, decision tree, random forest, and artificial neural network) were used to determine whether a patient's personal information could be acc

SUBMITTER: Kweon S 

PROVIDER: S-EPMC7445622 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature