Breast cancer prediction using different machine learning methods applying multi factors.

ABSTRACT:

Objective

Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors.

Methods

The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated.

Result

Compared to other methods random forest (RF) has higher performance (

SUBMITTER: Nazari E 

PROVIDER: S-EPMC11798178 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature