Differential Predictive Modeling for Racial Disparities in Breast Cancer

Abstract

The primary objective of disparities research is to model the differences across multiple groups and identify the groups that behave significantly different from each other. Independently generating various decision trees for different subsets of the data will not allow us to study the impact of the various attributes on these different subgroups. We propose a novel technique for inducing similar decision trees for different subpopulations and also develop a new distance metric between two decision trees which measures the difference in the underlying data distributions of these subgroups. The proposed framework is evaluated by analyzing the racial disparities in breast cancer. Our method was able to rank different populations with respect to the disparity and detect the attributes that are most responsible for such differences.

Constrained Logistic Regression for Discriminative Pattern Mining

“…The binary datasets are represented by triplet (dataset, attributes, instances). The UCI datasets used are (blood, 5, 748), (liver, 6, 345), (diabetes, 8, 768), (gamma, 11, 19020), and (heart, 22,267). Synthetic datasets used in our work have 500,000 to 1 million tuples.…”

“…(1) Dataset Distribution Differences -Despite the importance of the problem, only a small amount of work is available in describing the differences between two data distributions. Earlier approaches for measuring the deviation between two datasets used simple data statistics after decomposing the feature space into smaller regions using tree based models [22,12]. However, the final result obtained is a data-dependent measure and do not give any understanding about the features responsible for measuring that difference.…”

“…In other words, the experts want to find the locations where the difference in the predictive (cancer) models for the two racial groups is higher and the locations where such difference is negligible. Depending on such information, more health care initiatives will be organized in certain locations to reduce the racial discriminations in cancer patients [22].…”

Emerging trend of big data analytics in bioinformatics: a literature review

“…Haller et al 2012Gene expression classification using SVM Vanitha et al (2015) Ortholog detection in yeast species using imbalanced classification approaches including SVM Galpert et al (2015) Evolutionary feature selection using SVM and other techniques using MapReduce Peralta et al (2015) Genomic feature learning using SVMrecursive feature elimination algorithm Anaissi et al (2016) Decision trees Employing decision tree learning for processing of large datasets Hall et al (1998) RainForest, a framework supporting construction of fast decision tree for classification of large datasets Johannes Gehrke et al 2000Predictive decision tree model for analysing racial disparities in breast cancer Palit et al (2009) A streaming parallel decision tree algorithm for classification of largescale datasets and streaming data…”