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Iliou Machine Learning Data Preprocessing Method for Stress Level Prediction

  • Theodoros Iliou
  • Georgia KonstantopoulouEmail author
  • Ioannis Stephanakis
  • Konstantinos Anastasopoulos
  • Dimitrios Lymberopoulos
  • George Anastassopoulos
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 519)

Abstract

Data pre-processing is an important step in the data mining process. Data preparation and filtering steps can take considerable amount of processing time. Data pre-processing includes cleaning, normalization, transformation, feature extraction and selection. In this paper, Iliou and PCA data preprocessing methods evaluated in a data set of 103 students, aged 18–25, who were experiencing anxiety problems. The performance of Iliou and PCA data preprocessing methods was evaluated using the 10-fold cross validation method assessing seven classification algorithms, IB1, J48, Random Forest, MLP, SMO, JRip and FURIA, respectively. The classification results indicate that Iliou data preprocessing algorithm consistently and substantially outperforms PCA data preprocessing method, achieving 98.6% against 92.2% classification performance, respectively.

Keywords

Data preprocessing Machine learning Data mining Classification algorithms Stress Anxiety disorder Panic disorder 

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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Theodoros Iliou
    • 1
  • Georgia Konstantopoulou
    • 2
  • Ioannis Stephanakis
    • 3
  • Konstantinos Anastasopoulos
    • 4
  • Dimitrios Lymberopoulos
    • 4
  • George Anastassopoulos
    • 1
  1. 1.Medical Informatics Laboratory, Medical SchoolDemocritus University of ThraceAlexandroupolisGreece
  2. 2.Special Office for Health Consulting ServicesUniversity of PatrasPatrasGreece
  3. 3.Hellenic Telecommunication Organization S.A. (OTE)AthensGreece
  4. 4.Department of Electrical Engineer, Wire Communications LaboratoryUniversity of PatrasPatrasGreece

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