title: Abductive machine learning for modeling and predicting the educational score in school health surveys.
abstract: The use of modern abductive machine learning techniques is described for modeling and predicting outcome parameters in terms of input parameters in medical survey data. The AIM (Abductory Induction Mechanism) abductive network machine-learning tool is used to model the educational score in a health survey of 2,720 Albanian primary school children. Data included the child's age, gender, vision, nourishment, parasite infection, family size, parents' education, and educational score. Models synthesized by training on just 100 cases predict the educational score output for the remaining 2,620 cases with 100% accuracy. Simple models represented as analytical functions highlight global relationships and trends in the survey population. Models generated are quite robust, with no change in the basic model structure for a 10-fold increase in the size of the training set. Compared to other statistical and neural network approaches, AIM provides faster and highly automated model synthesis, requiring little or no user intervention.
authors.affiliation: Energy Research Laboratory, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. radwan@dpc.kfupm.edu.sa
authors.forename: R E
authors.initials: RE
authors.lastname: Abdel-Aal
authors.forename: A M
authors.initials: AM
authors.lastname: Mangoud
mesh_terms: D000124:Achievement; D002648:Child; D003198:Computer Simulation; D004493:Education; D005260:Female; D006306:Health Surveys; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D012572:School Health Services