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A Comparison Between Classification Algorithms for Postmenopausal Osteoporosis Prediction in Tunisian Population

  • Naoual GuannoniEmail author
  • Rim Sassi
  • Walid Bedhiafi
  • Mourad Elloumi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9832)

Abstract

In this paper, we make an experimental study to compare the performances of different data mining classification algorithms for predicting osteoporosis in Tunisian postmenopausal women. This study aims to identify the best algorithms with the optimum classification parameters values and to determine the most important risk factors that have a significant impact on the osteoporosis occurrence. The obtained results show that Support Vector Machine (SVM) classifier and Artificial Neural Network (ANN) classifier give the best classification performances when dealing with the three bone statuses (normal, osteopenia, osteoporosis). On the other hand, the decision tree classifier C4.5 enables to extract the most important risk factors for osteoporosis occurrence. The selected risk factors are validated by biologists.

Keywords

Data mining Classification algorithms comparison Optimum parameters Prediction Osteoporosis risk factors 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Naoual Guannoni
    • 1
    Email author
  • Rim Sassi
    • 2
  • Walid Bedhiafi
    • 2
  • Mourad Elloumi
    • 1
  1. 1.Laboratory of Technologies of Information and Communication and Electrical Engineering (LaTlCE), National Superior School of Engineers of Tunis (ENSIT)University of TunisTunisTunisia
  2. 2.LR05ES05 Laboratory of Genetic, Immunology and Human Pathologies (LGIPH), Faculty of Sciences of TunisUniversité de Tunis El ManarTunisTunisia

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