C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Classification of smoking cessation status with a backpropagation neural network
Journal of Biomedical Informatics
Efficient discovery of risk patterns in medical data
Artificial Intelligence in Medicine
Algorithmic Prediction of Health-Care Costs
Operations Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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The use of the methods of Knowledge Discovery in Databases (KDD) in the domain of public health is still topical. One of the major reasons for its increasing use is the need for an efficient processing of the increasing volumes of data. The aim of our contribution is to analyze the possibilities of the usage of these methods to identify the groups of school-aged children with a high probability of risky behavior. The obtained results are useful for the formation of models applicable for more efficient identification of target groups of prevention programs. In this work we use Slovak national dataset from the international study Health Behaviour in School-Aged Children. The used machine learning methods were Support Vector Machine, Naïve Bayes Classifier and the J48 machine learning algorithm. The results suggest promising possibilities for the use of the machine learning methods to develop classification models useful for public health.