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Suicide risk analysis using deep neural networks

Rok Hudobivnik (2017) Suicide risk analysis using deep neural networks. EngD thesis.

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    The goal of this thesis was to train a neural network to classify between two groups of people: those who have or have not committed suicide, based on the received biological data set. With the analysis of this data set further research could be performed with a goal of pre-emptive suicide prevention. In the experiments in this thesis, I achieved the average classification accuracy of 71,4 % and standard deviation of 2,33 %. The thesis deals with two distinct problems, first with the problem of missing values, that in the end proved to be the deciding factor for the limitations of the classification accuracy. Second, the problem of finding the optimal configuration of the neural network for this data set. The results and conclusions of this thesis are generally in agreement with other research done on this particular data set.

    Item Type: Thesis (EngD thesis)
    Keywords: Deep learning, neural nets, suicide
    Number of Pages: 45
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    akad. prof. dr. Ivan Bratko77Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537529283)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3930
    Date Deposited: 13 Sep 2017 10:27
    Last Modified: 22 Sep 2017 09:39
    URI: http://eprints.fri.uni-lj.si/id/eprint/3930

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