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Iliou Machine Learning Data Preprocessing Method for Suicide Prediction from Family History

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 559)


As real world data tends to be incomplete, noisy and inconsistent, data preprocessing is an important issue for data mining. Data preparation includes data cleaning, data integration, data transformation and data reduction. In this paper, Iliou preprocessing method is compared with Principal Component Analysis in suicide prediction according to family history. The dataset consists of 360 students, aged 18 to 24, who were experiencing family history problems. The performance of Iliou and Principal Component Analysis data preprocessing methods was evaluated using the 10-fold cross validation method assessing ten classification algorithms, IB1, J48, Random Forest, MLP, SMO, JRip, RBF, Naïve Bayes, AdaBoostM1 and HMM, respectively. Experimental results illustrate that Iliou data preprocessing algorithm outperforms Principal Component Analysis data preprocessing method, achieving 100% against 71.34% classification performance, respectively. According to the classification results, Iliou preprocessing method is the most suitable for suicide prediction.


  • Data preprocessing
  • Machine learning
  • Data mining
  • Classification algorithms
  • Suicide, family history

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  • DOI: 10.1007/978-3-030-19823-7_43
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Correspondence to George Anastassopoulos .

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Iliou, T. et al. (2019). Iliou Machine Learning Data Preprocessing Method for Suicide Prediction from Family History. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2019. IFIP Advances in Information and Communication Technology, vol 559. Springer, Cham.

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