2021/11/05 by Catherine Wanjiru, William Ogallo, Wanjiru, Catherine +10
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Data-Driven Disease Surveillance #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Pneumonia and Respiratory Infections #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2111.03495
openalex publication_date 2021/11/05 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
An automated feature selection pipeline was developed using several state-of-the-art feature selection techniques to select optimal features for Differentiating Patterns of Care (DPOC). The pipeline included three types of feature selection techniques; Filters, Wrappers and Embedded methods to select the top K features. Five different datasets with binary dependent variables were used and their different top K optimal features selected. The selected features were tested in the existing multi-dimensional subset scanning (MDSS) where the most anomalous subpopulations, most anomalous subsets, propensity scores, and effect of measures were recorded to test their performance. This performance was compared with four similar metrics gained after using all covariates in the dataset in the MDSS pipeline. We found out that despite the different feature selection techniques used, the data distribution is key to note when determining the technique to use.