How Health Managers Can Use Data Mining for Predicting Individuals’ Risks of Contracting Nosocomial Pneumonia

Abstract

This article explains how managers can use a new data-mining technique for solving problems related to individual risks of contracting nosocomial pneumonia. Using the genetic algorithm, a search technique provides practitioners with an optimal choice of parameters for Gini boosting type decision tree models. Thus, managers and technicians can choose better models. These new parameters are genetically controlled: number of trees, depth of trees, trimming factor, cross-validation (to avoid overfitting), proportion of the population used, and the minimum size to split a node. This technique has been satisfactorily tested on health data.

KEYWORDS:

• nosocomial pneumonia
• boosting
• genetic algorithm
• overlearning
• tree
• health management