FACTOR ANALYSIS AND PREDICTION OF SUICIDE RATES BY COUNTRIES BASED ON DEEP LEARNING MODELS
DOI:
https://doi.org/10.24867/20BE08BatinicKeywords:
time series, RNN, LSTM, regressionAbstract
Suicide is one of the leading causes of death worldwide, which is highly worrying. Тhis paper applies artificial intelligence to analyze the factors that might influence suicide and predict the suicide rate for different groups. Several data sets containing different factors that might influence the countries' suicide rates were collected to analyze their importance. These factors include country, gender, age group, age, and unemployment rate. Multiple prediction approaches were applied to the preprocessed data: (1) a traditional approach (multiple regression models) and (2) a time series approach (using the LSTM model).
References
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[2] J. D. Ribeiro, J. C. Franklin, K. R. Fox, K. H. Bentley, E. M. Kleiman, B. P. Chang, and M. K. Nock, “Self-Injurious Thoughts and Behaviors as Risk Factors for Future Suicide Ideation, Attempts, and Death: a Meta-Analysis of Longitudinal Studies,” Psychological Medicine, vol. 46, no. 2, pp. 225–236, 2016. DOI: 10.1017/S0033291715001804
[3] Imran Amin, Sobia Syed, Prediction of Suicide Causes in India using Machine Learning, Journal of Independent Studies and Research (JISR), Volume 15, Issue No 2, 2017.
[4] Jhansi lakshmi Durga Nunna, Akila Rani M., B. V. Ram Kumar, Design of Machine Learning based Suicide Rate Prediction System, International Journal of Scientific Research and Review, Volume 8, Issue 4, 2019
[5] Qiang Jiang, Chenglin Tang, Stock price forecast based on LSTM neural network, International Conference on Management Science and Engineering Management, Springer, pp.393-408, 2018.
[6] https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016
[7] https://databank.worldbank.org/source/world-development-indicators
[8] https://www.kaggle.com/psterk/income-inequality?select=gini.csv
[9] https://ourworldindata.org/marriages-and-divorces
[2] J. D. Ribeiro, J. C. Franklin, K. R. Fox, K. H. Bentley, E. M. Kleiman, B. P. Chang, and M. K. Nock, “Self-Injurious Thoughts and Behaviors as Risk Factors for Future Suicide Ideation, Attempts, and Death: a Meta-Analysis of Longitudinal Studies,” Psychological Medicine, vol. 46, no. 2, pp. 225–236, 2016. DOI: 10.1017/S0033291715001804
[3] Imran Amin, Sobia Syed, Prediction of Suicide Causes in India using Machine Learning, Journal of Independent Studies and Research (JISR), Volume 15, Issue No 2, 2017.
[4] Jhansi lakshmi Durga Nunna, Akila Rani M., B. V. Ram Kumar, Design of Machine Learning based Suicide Rate Prediction System, International Journal of Scientific Research and Review, Volume 8, Issue 4, 2019
[5] Qiang Jiang, Chenglin Tang, Stock price forecast based on LSTM neural network, International Conference on Management Science and Engineering Management, Springer, pp.393-408, 2018.
[6] https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016
[7] https://databank.worldbank.org/source/world-development-indicators
[8] https://www.kaggle.com/psterk/income-inequality?select=gini.csv
[9] https://ourworldindata.org/marriages-and-divorces
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Published
2022-11-05
Issue
Section
Electrotechnical and Computer Engineering
