Fei Deng

The paper evaluates ML model fairness in classifying survival outcomes for colorectal and lung cancer patients across sociodemographic groups using TCGA data, finding biases and proposing methods to reduce disparities.

This study compares the performance and efficiency of four machine learning algorithms on a large rectangular biomedical dataset, finding similar accuracy but varying efficiency with dimension reduction.

This study compares random forest and multinomial models for predicting 6-category cause of death in prostate cancer patients, finding random forest achieves 59.1% accuracy versus 50.4% for multinomial models.