Research Article
Year 2021,
Volume: 5 Issue: 3, 444 - 451, 25.12.2021
Abstract
Aim: The aim of this study is to classify the obtained data correctly using machine learning algorithms.
Material and Methods: Happiness, life satisfaction and hopelessness scales with personal information
form were applied to 195 patients who came to the psychiatry clinic and wanted to receive psychological
treatment due to their anxiety, depression and stress complaints. In this classification, theh happiness
core was chosen as the dependent variable and the factors affecting this variable were determined by
different methods such as training, test, and cross- validation.
Results: KA-RF (0.9180) gave the most successful classification result among decision tree algorithms
for k = 10 value. This result is supported by the criteria RMSE (0.2810), ROC area (0.9760) and
MCC (0.8400). In addition, the variables that most affect the level of happiness or unhappiness of the
participants in the study were found to be life satisfaction, age, and the ability to cope with difficulties,
respectively.
Conclusion: In line with the findings obtained as a result, it was determined that the effects of
environmental and social factors as well as the positive effects of especially living spaces were found in
the treatment of anxiety, depression and stress-related disorders.
References
- 1. Ulaş H, Binbay T İ, Alptekin K. Klinik Psikiyatri Araştırmalarında Maddi Çıkar Çatışması: Bir Gözden Geçirme. Türk Psikiyatri Dergisi. 2008: 19(4):418-426.
- 2. Baltaş Z, Baltaş A. Stres ve Başaçıkma Yolları, Remzi Kitabevi, İstanbul, 2004.
- 3. Who. Depression. http://www.who.int/mental_health/management/depression /definition/en/print.htm 2009.
- 4. Cox RH. Sport Psychology: Concepts and Applications. 7th Edition, New York: McGraw-Hill, 2012: 297-298.
- 5. Frederick C. Effects of natural vs. human induced violence upon victims. Evaluation and change, 1980: 71-75.
- 6. Doğan O. Ruhsal Bozuklukların Epidemolojisi. Cumhuriyet Üniveritesi Tıp Fakültesi Psikiyatri A.B.D Dilek Matbaası, Sivas, 1995.
- 7. Passer M W, Smith R E. Psychology: The science of mind and behavi¬our. Boston: McGraw-Hill Higher Education, 2008.
- 8. Ulrich R. Natural versus urban scenes: Some psychological effects. Environment and behavior. 1981: 13 (5), 523-556.
- 9. Ataç E. Suçun Kentsel Mekândaki Algısı: Güvensizlik Hissi. Dosya: Kent ve Suç. TMMOB Mimarlar Odası Ankara Şubesi Bülten 55, 2007.
- 10. Karalar R, Kiracı H. Tüketim Düşüncesi. Dumlupınar Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 2011: 30: 63-76.
- 11. Rachman S. Anxiety. Hove: Psychology Press; New York: Taylor & Francis, 2004.
- 12. Freud S. Mouning and melancholia. In J. Strachey (Ed. and trans.), Standart edition of the complete psychological works of Sigmund Freud. London: Hogarth Press, 1957.
- 13. Beck A T. Cognitive therapy and the emotinal disorders. New York: International Universities Press, 1976.
- 14. Wallace R K and et al. The effects of the transcendental meditation and TM-Siddhi program on the aging process. International Journal of Neuroscience. 1982: 16: 53-58.
- 15. Han J, Kamber M and Pei J. Data Mining: Concepts and Techniques. 3rd Edition, Morgan Kaufmann. 2011.
- 16. Rokach L, Maimon O. Decision Trees. Data Mining and Knowledge Discovery Handbook, Springer, 2005, 165-192.
- 17. Dangare C S, Apte S S. Improved study of heart disease prediction system using data mining classification Techniques. International Journal of Computer Applications, 2012: 47(10): 44-48.
- 18. Quinlan J R. C4.5: programs for machine learning. San Mateo, California: Morgan Kaufman publishers, 2014.
- 19. Kama F E. Yaşam ortamının insan psikolojisi üzerine etkileri. Yüksek lisans tezi. F.Ü. Fen Bilimleri Enstitüsü 2019.
- 20. Breiman L and et al. Classification and regression trees. CRC press, 1984.
- 21. Erpolat S, Öz E. Kanser Verilerinin Sınıflandırılmasında Yapay Sinir Ağları İle Destek Vektör Makineleri'nin Karşılaştırılması. İstanbul Aydın Üniversitesi Dergisi. 2010: 2(5): 71-83.
- 22. Filiz E, Oz E. Finding the Best Algorithms and Effective Factors in Classification of Turkish Science Student Success. Journal Of Baltic Science Education. 2019: 18(2): 239-253.
- 23. Breiman L. Random forests. Machine learning. 2001: 45(1): 5-32.
- 24. Chen X W, Liu M. Prediction of protein–protein interactions using random decision forest framework. Bioinformatics. 2005: 21(24): 4394-4400.
- 25. Kalmegh S. Analysis of WEKA data mining algorithm REPTree, Simple CART and RandomTree for classification of Indian news. International Journal of Innovative Science, Engineering & Technology. 2015: 2( 2): 438-446.
- 26. Srinivasan D B, Mekala P. Mining Social Networking Data for Classification Using REPTree. International Journal of Advance Research in Computer Science and Management Studies, 2014: 2(10): 155-160.
- 27. Frank E, Hall M Z, and Witten I H. The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, 2016.
- 28. Diener E, Suh ME, Lucas ER. and Smith H. Subjective Well-Being: Three Decades of Progress. Psychological Bulletin. 1999: 125 (2): 276–302.
- 29. Michalos CA. Education, Happiness and Well-Being. Social Indicators Research. 2008: 87(3): 347-366.
- 30. Kangal A. Mutluluk Üzerine Kavramsal Bir Değerlendirme ve Türk Hane halkı için Bazı Sonuçlar. Electronic Journal of Social Sciences. 2013: 12(44): 214-233.
- 31. Azar A T and et al. A random forest classifier for lymph diseases. Computer Methods and Programs in Biomedicine. 2014: 113(2): 465-473.
- 32. Steele A J and et al. Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. PloS one. 2018: 13(8), e0202344.
- 33. Shrestha A and et al. Mental health risk adjustment with clinical categories and machine learning. Health Services Research, 2018: 53: 3189-3206.
- 34. Buettner R, Schunter M. Efficient machine learning based detection of heart disease. IEEE International Conference on E-health Networking, Application & Services (HealthCom). 2019: 1-6.
Year 2021,
Volume: 5 Issue: 3, 444 - 451, 25.12.2021
Abstract
Amaç: Bu çalışmanın amacı, elde edilen verileri farklı makine öğrenmesi algoritmaları yardımıyla
sınıflandırmaktır.
Gereç ve Yöntemler: Psikiyatri polikliniğine gelen anksiyete, depresyon ve stres şikâyetlerinden dolayı
psikolojik tedavi almak isteyen 195 hastaya mutluluk, yaşam doyumu ve umutsuzluk ölçekleri ve kişisel
bilgi formu uygulanmıştır. Bu sınıflandırmada bağımlı değişken olarak mutluluk seçilmiş ve bu değişkeni
etkileyen faktörler eğitim, test ve çapraz doğrulama gibi farklı yöntemlerle belirlenmiştir.
Bulgular: k=10 değeri için karar ağacı algoritmaları arasında en başarılı sınıflandırma sonucunu KARF
(0,9180) vermiştir. Bu sonucu RMSE (0,2810), ROC alanı (0,9760) ve MCC (0,8400) kriterleri
desteklemektedir. Ayrıca çalışmaya katılan bireylerin mutlu ya da mutsuz olma düzeylerini en çok
etkileyen değişkenler sırasıyla yaşam doyumu, yaş ve sıkıntılarla baş etme becerisi olarak bulunmuştur.
Sonuç: Sonuç olarak, elde edilen bulgular doğrultusunda insanların yaşam alanlarının başta anksiyete,
depresyon ve strese bağlı rahatsızlıklarının tedavisinde özellikle yaşam alanlarının olumlu etkilerinin
yanı sıra çevresel ve sosyal faktörlerin etkilerinin de bulunduğu tespit edilmiştir.
Keywords
References
- 1. Ulaş H, Binbay T İ, Alptekin K. Klinik Psikiyatri Araştırmalarında Maddi Çıkar Çatışması: Bir Gözden Geçirme. Türk Psikiyatri Dergisi. 2008: 19(4):418-426.
- 2. Baltaş Z, Baltaş A. Stres ve Başaçıkma Yolları, Remzi Kitabevi, İstanbul, 2004.
- 3. Who. Depression. http://www.who.int/mental_health/management/depression /definition/en/print.htm 2009.
- 4. Cox RH. Sport Psychology: Concepts and Applications. 7th Edition, New York: McGraw-Hill, 2012: 297-298.
- 5. Frederick C. Effects of natural vs. human induced violence upon victims. Evaluation and change, 1980: 71-75.
- 6. Doğan O. Ruhsal Bozuklukların Epidemolojisi. Cumhuriyet Üniveritesi Tıp Fakültesi Psikiyatri A.B.D Dilek Matbaası, Sivas, 1995.
- 7. Passer M W, Smith R E. Psychology: The science of mind and behavi¬our. Boston: McGraw-Hill Higher Education, 2008.
- 8. Ulrich R. Natural versus urban scenes: Some psychological effects. Environment and behavior. 1981: 13 (5), 523-556.
- 9. Ataç E. Suçun Kentsel Mekândaki Algısı: Güvensizlik Hissi. Dosya: Kent ve Suç. TMMOB Mimarlar Odası Ankara Şubesi Bülten 55, 2007.
- 10. Karalar R, Kiracı H. Tüketim Düşüncesi. Dumlupınar Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 2011: 30: 63-76.
- 11. Rachman S. Anxiety. Hove: Psychology Press; New York: Taylor & Francis, 2004.
- 12. Freud S. Mouning and melancholia. In J. Strachey (Ed. and trans.), Standart edition of the complete psychological works of Sigmund Freud. London: Hogarth Press, 1957.
- 13. Beck A T. Cognitive therapy and the emotinal disorders. New York: International Universities Press, 1976.
- 14. Wallace R K and et al. The effects of the transcendental meditation and TM-Siddhi program on the aging process. International Journal of Neuroscience. 1982: 16: 53-58.
- 15. Han J, Kamber M and Pei J. Data Mining: Concepts and Techniques. 3rd Edition, Morgan Kaufmann. 2011.
- 16. Rokach L, Maimon O. Decision Trees. Data Mining and Knowledge Discovery Handbook, Springer, 2005, 165-192.
- 17. Dangare C S, Apte S S. Improved study of heart disease prediction system using data mining classification Techniques. International Journal of Computer Applications, 2012: 47(10): 44-48.
- 18. Quinlan J R. C4.5: programs for machine learning. San Mateo, California: Morgan Kaufman publishers, 2014.
- 19. Kama F E. Yaşam ortamının insan psikolojisi üzerine etkileri. Yüksek lisans tezi. F.Ü. Fen Bilimleri Enstitüsü 2019.
- 20. Breiman L and et al. Classification and regression trees. CRC press, 1984.
- 21. Erpolat S, Öz E. Kanser Verilerinin Sınıflandırılmasında Yapay Sinir Ağları İle Destek Vektör Makineleri'nin Karşılaştırılması. İstanbul Aydın Üniversitesi Dergisi. 2010: 2(5): 71-83.
- 22. Filiz E, Oz E. Finding the Best Algorithms and Effective Factors in Classification of Turkish Science Student Success. Journal Of Baltic Science Education. 2019: 18(2): 239-253.
- 23. Breiman L. Random forests. Machine learning. 2001: 45(1): 5-32.
- 24. Chen X W, Liu M. Prediction of protein–protein interactions using random decision forest framework. Bioinformatics. 2005: 21(24): 4394-4400.
- 25. Kalmegh S. Analysis of WEKA data mining algorithm REPTree, Simple CART and RandomTree for classification of Indian news. International Journal of Innovative Science, Engineering & Technology. 2015: 2( 2): 438-446.
- 26. Srinivasan D B, Mekala P. Mining Social Networking Data for Classification Using REPTree. International Journal of Advance Research in Computer Science and Management Studies, 2014: 2(10): 155-160.
- 27. Frank E, Hall M Z, and Witten I H. The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, 2016.
- 28. Diener E, Suh ME, Lucas ER. and Smith H. Subjective Well-Being: Three Decades of Progress. Psychological Bulletin. 1999: 125 (2): 276–302.
- 29. Michalos CA. Education, Happiness and Well-Being. Social Indicators Research. 2008: 87(3): 347-366.
- 30. Kangal A. Mutluluk Üzerine Kavramsal Bir Değerlendirme ve Türk Hane halkı için Bazı Sonuçlar. Electronic Journal of Social Sciences. 2013: 12(44): 214-233.
- 31. Azar A T and et al. A random forest classifier for lymph diseases. Computer Methods and Programs in Biomedicine. 2014: 113(2): 465-473.
- 32. Steele A J and et al. Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. PloS one. 2018: 13(8), e0202344.
- 33. Shrestha A and et al. Mental health risk adjustment with clinical categories and machine learning. Health Services Research, 2018: 53: 3189-3206.
- 34. Buettner R, Schunter M. Efficient machine learning based detection of heart disease. IEEE International Conference on E-health Networking, Application & Services (HealthCom). 2019: 1-6.
There are 34 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Health Care Administration |
| Journal Section | Research Article |
| Authors | |
| Publication Date | December 25, 2021 |
| Acceptance Date | July 8, 2021 |
| Published in Issue | Year 2021 Volume: 5 Issue: 3 |
Medical Journal of Western Black Sea is a scientific publication of Zonguldak Bulent Ecevit University Faculty of Medicine.
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