Machine Learning Preprocessing Method for Suicide Prediction

Abstract : The main objective of this study was to find a preprocessing method to enhance the effectiveness of the machine learning methods in datasets of mental patients. Specifically, the machine learning methods must have almost excellent classification results in patients with depression who have thoughts of suicide, in order to achieve the sooner the possible the appropriate treatment. In this paper, we establish a novel data preprocessing method for improving the prognosis’ possibilities of a patient suffering from depression to be leaded to the suicide. For this reason, the effectiveness of many machine learning classification algorithms is measured, with and without the use of our suggested preprocessing method. The experimental results reveal that our novel proposed data preprocessing method markedly improved the overall performance on initial dataset comparing with PCA and Evolutionary search feature selection methods. So this preprocessing method can be used for significantly boost classification algorithms performance in similar datasets and can be used for suicide tendency prediction.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.53-60, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_5〉
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Theodoros Iliou, Georgia Konstantopoulou, Mandani Ntekouli, Dimitrios Lymberopoulos, Konstantinos Assimakopoulos, et al.. Machine Learning Preprocessing Method for Suicide Prediction. Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.53-60, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_5〉. 〈hal-01557606〉

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