Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.

ABSTRACT: Classification of patient multicategory survival outcomes is important for personalized cancer treatments. Machine learning (ML) algorithms have increasingly been used to inform healthcare decisions, but these models are vulnerable to biases in data collection and algorithm creation. ML models have previously been shown to exhibit racial bias, but their fairness towards patients from different age and sex groups have yet to be studied. Therefore, we compared the multimetric performances of five ML models (random forests, multinomial logistic regression, linear support vector classifier, linear discriminant analysis, and multilayer perceptron) when classifying colorectal cancer patients (n = 589) of various age, sex, and racial groups using The Cancer Genome Atlas data. All five models exhi

SUBMITTER: Feng CH 

PROVIDER: S-EPMC12342732 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature