Araştırma Makalesi
BibTex RIS Kaynak Göster

Evaluation of Suicides in OECD Countries with Decision Trees and Bayesian Networks

Yıl 2023, Cilt: 10 Sayı: 1, 128 - 143, 31.03.2023
https://doi.org/10.17336/igusbd.961070

Öz

Suicides are an important problem that has been encountered in different frequencies from past to present. Individuals are trying to end their own lives by attempting suicide. There are many causes of suicide but its effects are devastating for all individuals in the community. This study aimed to examine that OECD member countries including Turkey of the relationship between the variables of per capita gross domestic product (GDP), unemployment, alcohol consumption, annual working time, divorce rate, and antidepressant use with suicide variable. Data mining classification techniques were used to examine the relationship. Among the data mining techniques, we used the algorithms giving the highest correct classification rates. As a result of the analyzes made, it was determined that all variables were associated with the suicide variable. Accordingly, it has been determined that the suicide rate may be high or moderate in countries with high unemployment level, alcohol consumption, annual working time, divorce rate, and antidepressant use and low GDP. Determining these features will be able to show what characteristics OECD countries will target in the measures they will take for suicide and the health and social policies they will develop.

Kaynakça

  • AKMAN, M., GENÇ, Y., & ANKARALI, H. (2011). Random Forests Yöntemi ve Sağlık Alanında Bir Uygulama. Türkiye Klinikleri Biyoistatistik, 3(1), 36-48.
  • AKPINAR, H. (2000). Veri tabanlarında bilgi keşfi ve veri madenciliği. İÜ İşletme Fakültesi Dergisi, 29(1), 1-22.
  • ALTUNKAYNAK, B. (2017). Veri Madenciliği Yöntemleri ve R Uygulamaları. Ankara: Seçkin Yayıncılık.
  • AMINI, P., AHMADINIA, H., POOROLAJAL, J., & AMIRI, M. M. (2016). Evaluating the high risk groups for suicide: a comparison of logistic regression, support vector machine, decision tree and artificial neural network. Iranian journal of public health, 45(9), 1179-1187.
  • ANDRÉS, A. R., HALICIOGLU, F. & YAMAMURA, E. (2011). Socio-economic determinants of suicide in Japan. The Journal of Socio-Economics, 40(6), 723-731.
  • BACA-GARCIA, E., PEREZ-RODRIGUEZ, M. M., SAIZ-GONZALEZ, D., BASURTE-VILLAMOR, I., SAIZ-RUIZ, J., LEIVA-MURILLO, J. M., ... & DE LEON, J. (2007). Variables associated with familial suicide attempts in a sample of suicide attempters. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 31(6), 1312-1316.
  • BAE, S. M., LEE, S. A., & LEE, S. H. (2015). Prediction by data mining, of suicide attempts in Korean adolescents: a national study. Neuropsychiatric disease and treatment, 11, 2367-2375.
  • BARTH, A., SÖGNER, L., GNAMBS, T., KUNDI, M., REINER, A. & WINKER, R. (2011). Socioeconomic factors and suicide: an analysis of 18 industrialized countries for the years 1983 through 2007. Journal of Occupational and Environmental Medicine, 53(3), 313-317.
  • BAZILA BANU, A., & THIRUMALAIKOLUNDUSUBRAMANIAN, P. (2018). Comparison of Bayes classifiers for breast cancer classification. Asian Pacific journal of cancer prevention: APJCP, 19(10), 2917-2920.
  • BEN-GAL, I. (2007). Bayesian Network. Encyclopedia of Statistics in Quality & Reliability. Wiley & Sons.
  • BERROUIGUET, S., BILLOT, R., LARSEN, M. E., LOPEZ-CASTROMAN, J., JAUSSENT, I., WALTER, M., ... & COURTET, P. (2019). An approach for data mining of electronic health record data for suicide risk management: database analysis for clinical decision support. JMIR mental health, 6(5), 1-11.
  • CHENG, J. & GREINER, R. (2001). Learning bayesian belief network classifiers: Algorithms and system. Conference of the Canadian Society for Computational Studies of Intelligence. Berlin: Springer.
  • ÇALIŞ, A., DURMAZ, K. İ. & GENCER, C. Uçak Seferlerindeki Rötarlari Etkileyen Faktörlerin Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 18. EYİ Özel Sayısı, 179-190.
  • ÇALIŞ, A., KAYAPINAR, S. & ÇETINYOKUŞ, T. (2014). Veri Madenciliğinde Karar Ağacı Algoritmaları ile Bilgisayar ve İnternet Güvenliği Üzerine Bir Uygulama. Endüstri Mühendisliği, 25(3), 2-19
  • ÇINICIOĞLU, E. N., ATALAY, M. & YORULMAZ, H. (2013). Trafik kazaları analizi için bayes ağları modeli. Bilişim Teknolojileri Dergisi, 6(2), 41.
  • DEMIRCI, Ş., KONCA, M., YETIM, B. & İLGÜN, G. (2019). Effect of economic crisis on suicide cases: An ARDL bounds testing approach. International Journal of Social Psychiatry, 66(1), 34-40.
  • DOGAN, S. & TURKOGLU, I. (2008). Iron-deficiency anemia detection from hematology parameters by using decision trees. International Journal of Science & Technology, 3(1), 85-92.
  • DOGRU, N., & SUBASI, A. (2018). Traffic accident detection using random forest classifier. 15th learning and technology conference. IEEE.
  • DURKHEIM, E. (2005). Suicide: A study in sociology. Routledge.
  • IBM. (2020). Random Forest. Erişim Tarihi: 09.12.2021, https://www.ibm.com/cloud/learn/random-forest
  • IBM. (2021). Predictor Importance. Erişim Tarihi: 09.12.2021, https://www.ibm.com/docs/en/spss-modeler/18.1.0?topic=SS3RA7_18.1.0/modeler_mainhelp_ client_ddita/clementine/idh_common_predictor_importance.html
  • ILGEN, M. A., DOWNING, K., ZIVIN, K., HOGGATT, K. J., KIM, H. M., GANOCZY, D., ... & VALENSTEIN, M. (2009). Exploratory data mining analysis identifying subgroups of patients with depression who are at high risk for suicide. The Journal of clinical psychiatry, 70(11), 1495-1500.
  • KAMAT, M. A., EDGAR, L., NIBLOCK, P., MCDOWELL, C. & KELLY, C. B. (2014). Association between antidepressant prescribing and suicide rates in OECD countries: an ecological study. Pharmacopsychiatry, 47(1), 18-21.
  • KHAZAEI, S., ARMANMEHR, V., NEMATOLLAHI, S., REZAEIAN, S. & KHAZAEI, S. (2017). Suicide rate in relation to the Human Development Index and other health related factors: A global ecological study from 91 countries. Journal of Epidemiology and Global Health, 7(2), 131-134.
  • KOYUNCUGIL, A. & ÖZGÜLBAŞ, N. (2009). Veri madenciliği: Tıp ve sağlık hizmetlerinde kullanımı ve uygulamaları. Bilişim Teknolojileri Dergisi, 2(2).
  • KUHN, M. & JOHNSON, K. (2013). Applied predictive modeling. New York: Springer.
  • LAANANI, M., GHOSN, W., JOUGLA, E. & REY, G. (2015). Impact of unemployment variations on suicide mortality in Western European countries (2000–2010). Journal of Epidemiology and Community Health, 69(2), 103-109.
  • OKADA, K. & SAMRETH, S. (2013). A study on the socio-economic determinants of suicide: Evidence from 13 European OECD countries. The Journal of Socio-Economics, 45, 78-85.
  • PIOTROWSKI, N. A. & HARTMANN, P. M. (2019). Suicide. Magill’s Medical Guide (Online Edition).
  • RATNER, B. (1998). CHAID for interpreting a logistic regression model. Journal of Targeting Measurement and Analysis For Marketing, 6, 215-226.
  • RYGIELSKI, C., WANG, J.-C. & YEN, D. C. (2002). Data Mining Techniques for Customer Relationship Management. Technology in Society, 24(4), 483-502.
  • SAVAŞ, S., TOPALOĞLU, N. & YILMAZ, M. (2012). Veri madenciliği ve Türkiye’deki uygulama örnekleri. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 11(21), 1-23.
  • SMITA, SHARMA, P. (2014). Use of data mining in various field: A survey paper. IOSR Journal of Computer Engineering, 16(3), 18-21.
  • SEYREK, İ. H. & ATA, H. A. (2010). Veri Zarflama Analizi ve Veri Madenciliği ile Mevduat Bankalarında Etkinlik Ölçümü. Journal of BRSA Banking & Financial Markets, 4(2).
  • SHI, G. (2013). Data mining and knowledge discovery for geoscientists. Elsevier.
  • STACK, S. (1990). New micro-level data on the impact of divorce on suicide, 1959-1980: A test of two theories. Journal of Marriage and the Family, 119-127.
  • TURE, M., TOKATLI, F. & KURT, I. (2009). Using Kaplan–Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4. 5 and ID3) in determining recurrence-free survival of breast cancer patients. Expert Systems with Applications, 36(2), 2017-2026.
  • U.S. MISSION TO THE ORGANIZATION FOR ECONOMIC COOPERATION & DEVELOPMENT. (2019). What is the OECD?. Erişim Tarihi: 04.01.2020, https://usoecd.usmission.gov/our-relationship/about-the-oecd/
  • WORLD HEALTH ORGANIZATION. (2018). Suicide. Erişim Tarihi: 04.01.2020, https://www.who.int/news-room/fact-sheets/detail/suicide
  • WU, X., KUMAR, V., QUINLAN, J. R., GHOSH, J., YANG, Q., MOTODA, H., ... & STEINBERG, D. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1), 1-37.
  • ZAKI, M. J., MEIRA JR, W. & MEIRA, W. (2014). Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press.

Karar Ağaçları ve Bayes Ağları ile OECD Ülkelerindeki İntiharların Değerlendirilmesi

Yıl 2023, Cilt: 10 Sayı: 1, 128 - 143, 31.03.2023
https://doi.org/10.17336/igusbd.961070

Öz

İntiharlar, geçmişten günümüze farklı sıklıklarda ve sürekli karşılaşılan önemli bir sorundur. Bireyler, intihar girişiminde bulunarak kendi yaşamına kendi isteğiyle son vermeye çalışmaktadır. İntiharın birçok nedeni bulunmakla birlikte etkileri toplumdaki tüm fertler için yıkıcı düzeydedir. Bu çalışmada, aralarında Türkiye’nin de bulunduğu OECD üye ülkelerinin kişi başı gayri safi yurtiçi hasıla (GSYİH), işsizlik, alkol tüketimi, yıllık çalışma süresi, boşanma ve antidepresan kullanımı değişkenlerinin intihar değişkeni ile ilişkisinin incelenmesi amaçlanmıştır. İlişkiyi incelemek için veri madenciliği sınıflandırma tekniklerinden faydalanılmıştır. Veri madenciliği teknikleri arasında ise en yüksek doğru sınıflandırma değerlerini veren algoritmalardan yararlanılmıştır. Yapılan analizler neticesinde tüm değişkenlerin intihar değişkeni ile ilişkili olduğu belirlenmiştir. Buna göre işsizlik düzeyi, alkol tüketimi, yıllık çalışma süresi, boşanma hızı ve antidepresan kullanımı yüksek ve GSYİH’si düşük düzeyde olan ülkelerde intihar hızının yüksek veya orta düzeyde olabileceği belirlenmiştir. Bu özelliklerin belirlenmesi OECD ülkelerinin intihara yönelik alacakları önlemlerde ve geliştirecekleri sağlık ve sosyal politikalarda hangi özellikleri hedef alabileceğini gösterebilecektir.

Kaynakça

  • AKMAN, M., GENÇ, Y., & ANKARALI, H. (2011). Random Forests Yöntemi ve Sağlık Alanında Bir Uygulama. Türkiye Klinikleri Biyoistatistik, 3(1), 36-48.
  • AKPINAR, H. (2000). Veri tabanlarında bilgi keşfi ve veri madenciliği. İÜ İşletme Fakültesi Dergisi, 29(1), 1-22.
  • ALTUNKAYNAK, B. (2017). Veri Madenciliği Yöntemleri ve R Uygulamaları. Ankara: Seçkin Yayıncılık.
  • AMINI, P., AHMADINIA, H., POOROLAJAL, J., & AMIRI, M. M. (2016). Evaluating the high risk groups for suicide: a comparison of logistic regression, support vector machine, decision tree and artificial neural network. Iranian journal of public health, 45(9), 1179-1187.
  • ANDRÉS, A. R., HALICIOGLU, F. & YAMAMURA, E. (2011). Socio-economic determinants of suicide in Japan. The Journal of Socio-Economics, 40(6), 723-731.
  • BACA-GARCIA, E., PEREZ-RODRIGUEZ, M. M., SAIZ-GONZALEZ, D., BASURTE-VILLAMOR, I., SAIZ-RUIZ, J., LEIVA-MURILLO, J. M., ... & DE LEON, J. (2007). Variables associated with familial suicide attempts in a sample of suicide attempters. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 31(6), 1312-1316.
  • BAE, S. M., LEE, S. A., & LEE, S. H. (2015). Prediction by data mining, of suicide attempts in Korean adolescents: a national study. Neuropsychiatric disease and treatment, 11, 2367-2375.
  • BARTH, A., SÖGNER, L., GNAMBS, T., KUNDI, M., REINER, A. & WINKER, R. (2011). Socioeconomic factors and suicide: an analysis of 18 industrialized countries for the years 1983 through 2007. Journal of Occupational and Environmental Medicine, 53(3), 313-317.
  • BAZILA BANU, A., & THIRUMALAIKOLUNDUSUBRAMANIAN, P. (2018). Comparison of Bayes classifiers for breast cancer classification. Asian Pacific journal of cancer prevention: APJCP, 19(10), 2917-2920.
  • BEN-GAL, I. (2007). Bayesian Network. Encyclopedia of Statistics in Quality & Reliability. Wiley & Sons.
  • BERROUIGUET, S., BILLOT, R., LARSEN, M. E., LOPEZ-CASTROMAN, J., JAUSSENT, I., WALTER, M., ... & COURTET, P. (2019). An approach for data mining of electronic health record data for suicide risk management: database analysis for clinical decision support. JMIR mental health, 6(5), 1-11.
  • CHENG, J. & GREINER, R. (2001). Learning bayesian belief network classifiers: Algorithms and system. Conference of the Canadian Society for Computational Studies of Intelligence. Berlin: Springer.
  • ÇALIŞ, A., DURMAZ, K. İ. & GENCER, C. Uçak Seferlerindeki Rötarlari Etkileyen Faktörlerin Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 18. EYİ Özel Sayısı, 179-190.
  • ÇALIŞ, A., KAYAPINAR, S. & ÇETINYOKUŞ, T. (2014). Veri Madenciliğinde Karar Ağacı Algoritmaları ile Bilgisayar ve İnternet Güvenliği Üzerine Bir Uygulama. Endüstri Mühendisliği, 25(3), 2-19
  • ÇINICIOĞLU, E. N., ATALAY, M. & YORULMAZ, H. (2013). Trafik kazaları analizi için bayes ağları modeli. Bilişim Teknolojileri Dergisi, 6(2), 41.
  • DEMIRCI, Ş., KONCA, M., YETIM, B. & İLGÜN, G. (2019). Effect of economic crisis on suicide cases: An ARDL bounds testing approach. International Journal of Social Psychiatry, 66(1), 34-40.
  • DOGAN, S. & TURKOGLU, I. (2008). Iron-deficiency anemia detection from hematology parameters by using decision trees. International Journal of Science & Technology, 3(1), 85-92.
  • DOGRU, N., & SUBASI, A. (2018). Traffic accident detection using random forest classifier. 15th learning and technology conference. IEEE.
  • DURKHEIM, E. (2005). Suicide: A study in sociology. Routledge.
  • IBM. (2020). Random Forest. Erişim Tarihi: 09.12.2021, https://www.ibm.com/cloud/learn/random-forest
  • IBM. (2021). Predictor Importance. Erişim Tarihi: 09.12.2021, https://www.ibm.com/docs/en/spss-modeler/18.1.0?topic=SS3RA7_18.1.0/modeler_mainhelp_ client_ddita/clementine/idh_common_predictor_importance.html
  • ILGEN, M. A., DOWNING, K., ZIVIN, K., HOGGATT, K. J., KIM, H. M., GANOCZY, D., ... & VALENSTEIN, M. (2009). Exploratory data mining analysis identifying subgroups of patients with depression who are at high risk for suicide. The Journal of clinical psychiatry, 70(11), 1495-1500.
  • KAMAT, M. A., EDGAR, L., NIBLOCK, P., MCDOWELL, C. & KELLY, C. B. (2014). Association between antidepressant prescribing and suicide rates in OECD countries: an ecological study. Pharmacopsychiatry, 47(1), 18-21.
  • KHAZAEI, S., ARMANMEHR, V., NEMATOLLAHI, S., REZAEIAN, S. & KHAZAEI, S. (2017). Suicide rate in relation to the Human Development Index and other health related factors: A global ecological study from 91 countries. Journal of Epidemiology and Global Health, 7(2), 131-134.
  • KOYUNCUGIL, A. & ÖZGÜLBAŞ, N. (2009). Veri madenciliği: Tıp ve sağlık hizmetlerinde kullanımı ve uygulamaları. Bilişim Teknolojileri Dergisi, 2(2).
  • KUHN, M. & JOHNSON, K. (2013). Applied predictive modeling. New York: Springer.
  • LAANANI, M., GHOSN, W., JOUGLA, E. & REY, G. (2015). Impact of unemployment variations on suicide mortality in Western European countries (2000–2010). Journal of Epidemiology and Community Health, 69(2), 103-109.
  • OKADA, K. & SAMRETH, S. (2013). A study on the socio-economic determinants of suicide: Evidence from 13 European OECD countries. The Journal of Socio-Economics, 45, 78-85.
  • PIOTROWSKI, N. A. & HARTMANN, P. M. (2019). Suicide. Magill’s Medical Guide (Online Edition).
  • RATNER, B. (1998). CHAID for interpreting a logistic regression model. Journal of Targeting Measurement and Analysis For Marketing, 6, 215-226.
  • RYGIELSKI, C., WANG, J.-C. & YEN, D. C. (2002). Data Mining Techniques for Customer Relationship Management. Technology in Society, 24(4), 483-502.
  • SAVAŞ, S., TOPALOĞLU, N. & YILMAZ, M. (2012). Veri madenciliği ve Türkiye’deki uygulama örnekleri. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 11(21), 1-23.
  • SMITA, SHARMA, P. (2014). Use of data mining in various field: A survey paper. IOSR Journal of Computer Engineering, 16(3), 18-21.
  • SEYREK, İ. H. & ATA, H. A. (2010). Veri Zarflama Analizi ve Veri Madenciliği ile Mevduat Bankalarında Etkinlik Ölçümü. Journal of BRSA Banking & Financial Markets, 4(2).
  • SHI, G. (2013). Data mining and knowledge discovery for geoscientists. Elsevier.
  • STACK, S. (1990). New micro-level data on the impact of divorce on suicide, 1959-1980: A test of two theories. Journal of Marriage and the Family, 119-127.
  • TURE, M., TOKATLI, F. & KURT, I. (2009). Using Kaplan–Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4. 5 and ID3) in determining recurrence-free survival of breast cancer patients. Expert Systems with Applications, 36(2), 2017-2026.
  • U.S. MISSION TO THE ORGANIZATION FOR ECONOMIC COOPERATION & DEVELOPMENT. (2019). What is the OECD?. Erişim Tarihi: 04.01.2020, https://usoecd.usmission.gov/our-relationship/about-the-oecd/
  • WORLD HEALTH ORGANIZATION. (2018). Suicide. Erişim Tarihi: 04.01.2020, https://www.who.int/news-room/fact-sheets/detail/suicide
  • WU, X., KUMAR, V., QUINLAN, J. R., GHOSH, J., YANG, Q., MOTODA, H., ... & STEINBERG, D. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1), 1-37.
  • ZAKI, M. J., MEIRA JR, W. & MEIRA, W. (2014). Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press.
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Şenol Demirci 0000-0001-8552-8151

Duygu İçen 0000-0002-7940-5064

Yayımlanma Tarihi 31 Mart 2023
Kabul Tarihi 14 Aralık 2021
Yayımlandığı Sayı Yıl 2023 Cilt: 10 Sayı: 1

Kaynak Göster

APA Demirci, Ş., & İçen, D. (2023). Karar Ağaçları ve Bayes Ağları ile OECD Ülkelerindeki İntiharların Değerlendirilmesi. İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi, 10(1), 128-143. https://doi.org/10.17336/igusbd.961070

Creative Commons Lisansı
İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi Creative Commons Atıf-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.