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Bağımlılık Yapıcı Madde Kullanımı Riskinin Makine Öğrenmesi Yöntemleriyle Tahmini

Yıl 2023, Cilt: 3 Sayı: 2, 1 - 13, 28.08.2023

Murad Naghiyev İclal Çetin Taş Yücel Tekin

Öz

Teknolojinin hızlı gelişimiyle birlikte, farklı alanlarda veri odaklı çalışmalar giderek artmaktadır. Bu çalışmalarda, makine öğrenme algoritmaları sıklıkla kullanılmakta ve özellikle tıp alanında erken tanı ve teşhis amaçlı kullanımı giderek yaygınlaşmaktadır. Makine öğrenme teknikleri, tıp alanında hastalıkların erken teşhis edilmesi, tedavi planlaması ve hastalık yönetiminde daha etkili ve doğru kararlar alınmasına yardımcı olmaktadır. Bu sayede hastaların sağlık durumları hakkında daha fazla bilgiye sahip olunarak, sağlık hizmetlerinin kalitesi ve verimliliği artırılmaktadır. Bu çalışmanın amacı, bağımlılık yapıcı madde kullanan kişilerin ileride başka hangi bağımlılık yapan maddeleri kullanma risklerinin olabileceğini makine öğrenmesi yöntemleri ile tahminlemektir. Bağımlılık yapıcı madde kullanımlarına ilişkin veri setinde yapılan uygulamada KNN (k-nearest neighbour), Gaussian SVM (support vector machine), karar ağacı (DT-decision tree) yöntemleri kullanılmış olup elde edilen sonuçlar incelendiğinde en yüksek başarı %90,60 ile Gaussian SVM yönteminden elde edilmiştir.

Anahtar Kelimeler

madde bağımlılığı makine öğrenmesi tahmin KNN SVM karar ağacı.

Kaynakça

  • 1. Open.lib.umn.edu. n.d. Drug Use in History. Retrieved from: https://open.lib.umn.edu/socialproblems/chapter/7-1-drug-use-in-history/.
  • 2. Wilson B. The effect of drugs on male sexual function and fertility. The Nurse Practitioner. 1991;16(9):12–24.
  • 3. NIDA. Drug Misuse and Addiction. Retrieved from https://nida.nih.gov/publications/drugs-brainsbehavior-science-addiction/drug-misuse-addiction 2020, July 13.
  • 4. Shaffer H. Understanding the means and objects of addiction: Technology, the internet, and gambling. Journal of Gambling Studies. 1996;12(4):461-9.
  • 5. Gökler R, Koçak R. Uyuşturucu ve madde bağımlılığı. Sosyal Bilimler Araştırmaları Dergisi. 2008;3(1):89-104.
  • 6. Schuckit M. Treatment of opioid-use disorders. New England Journal of Medicine. 2016;375(4):357-68. 7. Kumari D, Kilam S, Nath P, et al. Prediction of alcohol abused individuals using artificial neural network. Int. J. Inf. Tecnol. 2018;10:233–7.
  • 8. Prat G, Adan A. Influence of circadian typology on drug consumption, hazardous alcohol use, and hangover symptoms. Chronobiology International. 2011;28(3):248-57.
  • 9. Türkiye Cumhuriyeti İçişleri Bakanlığı Narkotik Suçlarla Mücadele Daire Başkanlığı. 2022. Türkiye Uyuşturucu Raporu 2022. Erişim adresi: (https://www.narkotik.pol.tr/kurumlar/narkotik.pol.tr/TUB%C4%B0M/Ulusal%20Yay%C4%B1nlar/Tu rkiye-Uyusturucu-Raporu-2022.pdf) 2022.
  • 10. Crane R. The most addictive drug, the most deadly substance: smoking cessation tactics for the busy clinician. Primary Care: Clinics in Office Practice. 2007;34(1):117-135.
  • 11.Rech M, Donahey E, Cappiello D, Greenhalgh E. New Drugs of Abuse. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy. 2014;35(2):189-197.
  • 12.Roh Y, Heo G, Whang E. A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering. 2019;33(4):1328-47.
  • 13.Zhang ZM, Tan JX, Wang F, et al. Early diagnosis of hepatocellular carcinoma using machine learning method. Frontiers in Bioengineering and Biotechnology. 2020;8:254.
  • 14. Salvatore C, Cerasa A, Battista P, Gilardi M, Quattrone A, Castiglioni I. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach. Frontiers in Neuroscience. 2015; 9:307.
  • 15. So A, Hooshyar D, Park KW, Lim HS. Early diagnosis of dementia from clinical data by machine learning techniques. Applied Sciences. 2017;7(7):651.
  • 16. Ashton C and Kamali F . Personality, lifestyles, alcohol and drug consumption in a sample of British medical students. Medical education.1995, 29(3), pp.187-192.
  • 17. Fehrman E, Muhammad AK, Mirkes EM, Egan V, Gorban AN. The five-factor model of personality and evaluation of drug consumption risk. In Data science. Springer, Cham. 2017, 231-242.
  • 18. Ferwerda B, Tkalčič M. Exploring the prediction of personality traits from drug consumption profiles. In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 2020, 2-5.
  • 19. Skóra MN, Pattij T, Beroun A, Kogias G, Mielenz D, de Vries T, et al. Personality driven alcohol and drug abuse: New mechanisms revealed. Neurosci Biobehav Rev. 2020;116:64-73.
  • 20. Franken, IH, Stam, CJ, Hendriks, VM, & van den Brink, W. (2004). Electroencephalographic power and coherence analyses suggest altered brain function in abstinent male heroin-dependent patients. Neuropsychobiology, 49(2), 105–110. https://doi.org/10.1159/000076419
  • 21. Fumiahru T, Neil SC, Benjamin HN. Electroencephalogram characteristics of autonomic arousals during sleep in healthy men. Clin. Neurophysiol. 2006;117:2490–2623.
  • 22. Subaşı A, Erçelbi E. Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine. 2005;78:77-90.
  • 23. Uzbay T. Madde Kötüye Kullanımı ve Bağımlılığı. Psikofarmakoloji. Yüksel N. (Ed.), Çizgi Tıp Kitabevi, Ankara, 2003, Yenilenmiş 2. Baskı, s. 485-520.
  • 24. Swift JK, Callahan JL, Vollmer BM. Preferences. Journal of Clinical Psychology. 2018;74(7):1009-18. 25. Kantarsic M. Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons Publishing, 2003.
  • 26. Köknel Ö. Bağımlılık Alkol ve Madde Bağımlılığı. (1. Baskı). İstanbul: Altın Kitap. 1998.
  • 27. Mansour A, Khacaturian H. Anatomy of CNS opioid receptors. Trend Neurosci. 1988;11(7):308–14.
  • 28. Macellan T, Lewis C. Drug dependence, a chronic medical illness: Implications for treatment, insurance, and outcomes evaluation. JAMA. 2000; 284(13):1689–95.
  • 29. Panksepp B, Lavhis GP. Rodent empathy and affective neuroscience. Neurosci Biobehav Rev. 2011;35(9):1854–85.
  • 30. Fehrman, E, Muhammad, AK, Mirkes, EM, Egan, V, Gorban, A.N. (2017). The Five Factor Model of Personality and Evaluation of Drug Consumption Risk. In: Palumbo, F., Montanari, A., Vichi, M. (eds) Data Science . Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55723-6_18.
  • 31. Nath P, Kilam S, Swetapadma A. A machine learning approach to predict volatile substance abuse for drug risk analysis. 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata. 2017, 255-258.
  • 32. Dumortier A, Beckjord E, Shiffman S, Sejdić E. Classifying smoking urges via machine learning. Comput Methods Programs Biomed. 2016;137:203-13.
  • 33. Hariharan B, Krithivasan R, Deborah A. Prediction of secondary school students’ alcohol addiction using random forest. International Journal of Computer Applications. 2016;149(6):21-5.
  • 34. Kharabsheh M, Meqdadi O, Alabed M, Veeranki S, Abbadi A, Alzyoud S. A machine learning approach for predicting nicotine dependence. International Journal of Advanced Computer Science and Applications. 2019;10(3):179-84.
  • 35.Yuan Y, Huang J, Yan K. Virtual reality therapy and machine learning techniques in drug addiction treatment. 2019 10th International Conference on Information Technology in Medicine and Education (ITME). doi:10.1109/itme.2019.00062 .2019.
  • 36. Ding X, Yang Y, Stein EA, Ross TJ. Combining Multiple Resting-State fMRI features during classification: Optimized frameworks and their application to nicotine addiction. Front Hum Neurosci. 2017;11:362.
  • 37. Fehrman,Elaine, Egan,Vincent, and Mirkes,Evgeny. (2016). Drug consumption (quantified). UCI Machine Learning Repository. https://doi.org/10.24432/C5TC7S
  • 38. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media. 2009.
  • 39. Fix, E., & Hodges, JL. (1989). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. International Statistical Review / Revue Internationale de Statistique, 57(3), 238–247. https://doi.org/10.2307/1403797
  • 40. Cristianini, N, Ricci, E. (2008). Support Vector Machines. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30162-4_415
  • 41. Bell, DE, Raiffa, H, & Tversky, A. (1988). Descriptive, normative, and prescriptive interactions in decision making. In D. E. Bell, H. Raiffa, & A. Tversky (Eds.), Decision making: Descriptive, normative, and prescriptive interactions (pp. 9–30). Cambridge University Press. https://doi.org/10.1017/CBO9780511598951.003
  • 42. Bishop, CM, & Nasrabadi, NM. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
  • 43. NIDA. 2023, May 30. Preface. Retrieved from https://nida.nih.gov/research-topics/addictionscience/drugs-brain-behavior-science-of-addiction on 2023, July 18
  • 44. Ghousi, R, Mehrani, S, & Momeni, M. (2012). Application of Data Mining Techniques in Drug Consumption Forecasting to Help Pharmaceutical Industry Production Planning.
  • 45. Motamedi E, Barile F and Tkalčič M. (2022). Prediction of Eudaimonic and Hedonic Orientation of Movie Watchers. Applied Sciences. 10.3390/app12199500. 12:19. (9500).
  • 46. Dede, Ş, Kayabaşı, H, Karagöz, F, Aker Karagöz, Y, & Savaş, A. (2017). Uyuşturucu Madde Kullanımına Bağlı Akut Böbrek Hasarı: Olgu Serisi [Acute Kidney Injury due to Drug Abuse: A Case Series]. Gaziosmanpaşa Taksim Training and Research Hospital.DOI: 10.5152/jarem.2017.988
  • 47. Bickel, WK, Yi, R, Mueller, ET, Jones, BA, & Christensen, DR. (2010). The Behavioral Economics of Drug Dependence: Towards the Consilience of Economics and Behavioral Neuroscience. In D. Self & J. Staley Gottschalk (Eds.), Behavioral Neuroscience of Drug Addiction. Current Topics in Behavioral Neurosciences, Vol. 3, s. 3-24. Springer. https://doi.org/10.1007/7854_2009_22

Prediction of Addictive Substance Usage Risk by using Machine Learning Methods

Yıl 2023, Cilt: 3 Sayı: 2, 1 - 13, 28.08.2023

Murad Naghiyev İclal Çetin Taş Yücel Tekin

Öz

With the rapid development of technology, data-driven studies are increasingly being carried out in various fields. Machine learning algorithms are commonly used in these studies, and their use for early diagnosis and detection in the medical field is becoming more widespread. Machine learning techniques help in the early diagnosis of diseases, treatment planning, and making more effective and accurate
decisions in disease management in the medical field. This leads to increased knowledge about patients' health conditions, thereby improving the quality and efficiency of healthcare services. The aim of this study is to predict which other addictive substances individuals who use addictive substances may use in the future using machine learning methods. KNN (knearest neighbor), Gaussian SVM (support vector machine), and decision tree methods were used in the application on the dataset of addictive substance use, and the results obtained were examined. The highest success rate was obtained with the Gaussian SVM method, which was 90.60%.

Anahtar Kelimeler

substance addiction machine learning prediction KNN SVM decision tree.

Kaynakça

  • 1. Open.lib.umn.edu. n.d. Drug Use in History. Retrieved from: https://open.lib.umn.edu/socialproblems/chapter/7-1-drug-use-in-history/.
  • 2. Wilson B. The effect of drugs on male sexual function and fertility. The Nurse Practitioner. 1991;16(9):12–24.
  • 3. NIDA. Drug Misuse and Addiction. Retrieved from https://nida.nih.gov/publications/drugs-brainsbehavior-science-addiction/drug-misuse-addiction 2020, July 13.
  • 4. Shaffer H. Understanding the means and objects of addiction: Technology, the internet, and gambling. Journal of Gambling Studies. 1996;12(4):461-9.
  • 5. Gökler R, Koçak R. Uyuşturucu ve madde bağımlılığı. Sosyal Bilimler Araştırmaları Dergisi. 2008;3(1):89-104.
  • 6. Schuckit M. Treatment of opioid-use disorders. New England Journal of Medicine. 2016;375(4):357-68. 7. Kumari D, Kilam S, Nath P, et al. Prediction of alcohol abused individuals using artificial neural network. Int. J. Inf. Tecnol. 2018;10:233–7.
  • 8. Prat G, Adan A. Influence of circadian typology on drug consumption, hazardous alcohol use, and hangover symptoms. Chronobiology International. 2011;28(3):248-57.
  • 9. Türkiye Cumhuriyeti İçişleri Bakanlığı Narkotik Suçlarla Mücadele Daire Başkanlığı. 2022. Türkiye Uyuşturucu Raporu 2022. Erişim adresi: (https://www.narkotik.pol.tr/kurumlar/narkotik.pol.tr/TUB%C4%B0M/Ulusal%20Yay%C4%B1nlar/Tu rkiye-Uyusturucu-Raporu-2022.pdf) 2022.
  • 10. Crane R. The most addictive drug, the most deadly substance: smoking cessation tactics for the busy clinician. Primary Care: Clinics in Office Practice. 2007;34(1):117-135.
  • 11.Rech M, Donahey E, Cappiello D, Greenhalgh E. New Drugs of Abuse. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy. 2014;35(2):189-197.
  • 12.Roh Y, Heo G, Whang E. A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering. 2019;33(4):1328-47.
  • 13.Zhang ZM, Tan JX, Wang F, et al. Early diagnosis of hepatocellular carcinoma using machine learning method. Frontiers in Bioengineering and Biotechnology. 2020;8:254.
  • 14. Salvatore C, Cerasa A, Battista P, Gilardi M, Quattrone A, Castiglioni I. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach. Frontiers in Neuroscience. 2015; 9:307.
  • 15. So A, Hooshyar D, Park KW, Lim HS. Early diagnosis of dementia from clinical data by machine learning techniques. Applied Sciences. 2017;7(7):651.
  • 16. Ashton C and Kamali F . Personality, lifestyles, alcohol and drug consumption in a sample of British medical students. Medical education.1995, 29(3), pp.187-192.
  • 17. Fehrman E, Muhammad AK, Mirkes EM, Egan V, Gorban AN. The five-factor model of personality and evaluation of drug consumption risk. In Data science. Springer, Cham. 2017, 231-242.
  • 18. Ferwerda B, Tkalčič M. Exploring the prediction of personality traits from drug consumption profiles. In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 2020, 2-5.
  • 19. Skóra MN, Pattij T, Beroun A, Kogias G, Mielenz D, de Vries T, et al. Personality driven alcohol and drug abuse: New mechanisms revealed. Neurosci Biobehav Rev. 2020;116:64-73.
  • 20. Franken, IH, Stam, CJ, Hendriks, VM, & van den Brink, W. (2004). Electroencephalographic power and coherence analyses suggest altered brain function in abstinent male heroin-dependent patients. Neuropsychobiology, 49(2), 105–110. https://doi.org/10.1159/000076419
  • 21. Fumiahru T, Neil SC, Benjamin HN. Electroencephalogram characteristics of autonomic arousals during sleep in healthy men. Clin. Neurophysiol. 2006;117:2490–2623.
  • 22. Subaşı A, Erçelbi E. Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine. 2005;78:77-90.
  • 23. Uzbay T. Madde Kötüye Kullanımı ve Bağımlılığı. Psikofarmakoloji. Yüksel N. (Ed.), Çizgi Tıp Kitabevi, Ankara, 2003, Yenilenmiş 2. Baskı, s. 485-520.
  • 24. Swift JK, Callahan JL, Vollmer BM. Preferences. Journal of Clinical Psychology. 2018;74(7):1009-18. 25. Kantarsic M. Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons Publishing, 2003.
  • 26. Köknel Ö. Bağımlılık Alkol ve Madde Bağımlılığı. (1. Baskı). İstanbul: Altın Kitap. 1998.
  • 27. Mansour A, Khacaturian H. Anatomy of CNS opioid receptors. Trend Neurosci. 1988;11(7):308–14.
  • 28. Macellan T, Lewis C. Drug dependence, a chronic medical illness: Implications for treatment, insurance, and outcomes evaluation. JAMA. 2000; 284(13):1689–95.
  • 29. Panksepp B, Lavhis GP. Rodent empathy and affective neuroscience. Neurosci Biobehav Rev. 2011;35(9):1854–85.
  • 30. Fehrman, E, Muhammad, AK, Mirkes, EM, Egan, V, Gorban, A.N. (2017). The Five Factor Model of Personality and Evaluation of Drug Consumption Risk. In: Palumbo, F., Montanari, A., Vichi, M. (eds) Data Science . Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55723-6_18.
  • 31. Nath P, Kilam S, Swetapadma A. A machine learning approach to predict volatile substance abuse for drug risk analysis. 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata. 2017, 255-258.
  • 32. Dumortier A, Beckjord E, Shiffman S, Sejdić E. Classifying smoking urges via machine learning. Comput Methods Programs Biomed. 2016;137:203-13.
  • 33. Hariharan B, Krithivasan R, Deborah A. Prediction of secondary school students’ alcohol addiction using random forest. International Journal of Computer Applications. 2016;149(6):21-5.
  • 34. Kharabsheh M, Meqdadi O, Alabed M, Veeranki S, Abbadi A, Alzyoud S. A machine learning approach for predicting nicotine dependence. International Journal of Advanced Computer Science and Applications. 2019;10(3):179-84.
  • 35.Yuan Y, Huang J, Yan K. Virtual reality therapy and machine learning techniques in drug addiction treatment. 2019 10th International Conference on Information Technology in Medicine and Education (ITME). doi:10.1109/itme.2019.00062 .2019.
  • 36. Ding X, Yang Y, Stein EA, Ross TJ. Combining Multiple Resting-State fMRI features during classification: Optimized frameworks and their application to nicotine addiction. Front Hum Neurosci. 2017;11:362.
  • 37. Fehrman,Elaine, Egan,Vincent, and Mirkes,Evgeny. (2016). Drug consumption (quantified). UCI Machine Learning Repository. https://doi.org/10.24432/C5TC7S
  • 38. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media. 2009.
  • 39. Fix, E., & Hodges, JL. (1989). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. International Statistical Review / Revue Internationale de Statistique, 57(3), 238–247. https://doi.org/10.2307/1403797
  • 40. Cristianini, N, Ricci, E. (2008). Support Vector Machines. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30162-4_415
  • 41. Bell, DE, Raiffa, H, & Tversky, A. (1988). Descriptive, normative, and prescriptive interactions in decision making. In D. E. Bell, H. Raiffa, & A. Tversky (Eds.), Decision making: Descriptive, normative, and prescriptive interactions (pp. 9–30). Cambridge University Press. https://doi.org/10.1017/CBO9780511598951.003
  • 42. Bishop, CM, & Nasrabadi, NM. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
  • 43. NIDA. 2023, May 30. Preface. Retrieved from https://nida.nih.gov/research-topics/addictionscience/drugs-brain-behavior-science-of-addiction on 2023, July 18
  • 44. Ghousi, R, Mehrani, S, & Momeni, M. (2012). Application of Data Mining Techniques in Drug Consumption Forecasting to Help Pharmaceutical Industry Production Planning.
  • 45. Motamedi E, Barile F and Tkalčič M. (2022). Prediction of Eudaimonic and Hedonic Orientation of Movie Watchers. Applied Sciences. 10.3390/app12199500. 12:19. (9500).
  • 46. Dede, Ş, Kayabaşı, H, Karagöz, F, Aker Karagöz, Y, & Savaş, A. (2017). Uyuşturucu Madde Kullanımına Bağlı Akut Böbrek Hasarı: Olgu Serisi [Acute Kidney Injury due to Drug Abuse: A Case Series]. Gaziosmanpaşa Taksim Training and Research Hospital.DOI: 10.5152/jarem.2017.988
  • 47. Bickel, WK, Yi, R, Mueller, ET, Jones, BA, & Christensen, DR. (2010). The Behavioral Economics of Drug Dependence: Towards the Consilience of Economics and Behavioral Neuroscience. In D. Self & J. Staley Gottschalk (Eds.), Behavioral Neuroscience of Drug Addiction. Current Topics in Behavioral Neurosciences, Vol. 3, s. 3-24. Springer. https://doi.org/10.1007/7854_2009_22
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yazılım Mühendisliği (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Murad Naghiyev 0000-0001-8885-0314

İclal Çetin Taş 0000-0002-1101-9773

Yücel Tekin Bu kişi benim 0000-0002-4565-5401

Erken Görünüm Tarihi 28 Ağustos 2023
Yayımlanma Tarihi 28 Ağustos 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 3 Sayı: 2

Kaynak Göster

APA Naghiyev, M., Çetin Taş, İ., & Tekin, Y. (2023). Bağımlılık Yapıcı Madde Kullanımı Riskinin Makine Öğrenmesi Yöntemleriyle Tahmini. Abant Sağlık Bilimleri Ve Teknolojileri Dergisi, 3(2), 1-13.
AMA Naghiyev M, Çetin Taş İ, Tekin Y. Bağımlılık Yapıcı Madde Kullanımı Riskinin Makine Öğrenmesi Yöntemleriyle Tahmini. SABİTED. Ağustos 2023;3(2):1-13.
Chicago Naghiyev, Murad, İclal Çetin Taş, ve Yücel Tekin. “Bağımlılık Yapıcı Madde Kullanımı Riskinin Makine Öğrenmesi Yöntemleriyle Tahmini”. Abant Sağlık Bilimleri Ve Teknolojileri Dergisi 3, sy. 2 (Ağustos 2023): 1-13.
EndNote Naghiyev M, Çetin Taş İ, Tekin Y (01 Ağustos 2023) Bağımlılık Yapıcı Madde Kullanımı Riskinin Makine Öğrenmesi Yöntemleriyle Tahmini. Abant Sağlık Bilimleri ve Teknolojileri Dergisi 3 2 1–13.
IEEE M. Naghiyev, İ. Çetin Taş, ve Y. Tekin, “Bağımlılık Yapıcı Madde Kullanımı Riskinin Makine Öğrenmesi Yöntemleriyle Tahmini”, SABİTED, c. 3, sy. 2, ss. 1–13, 2023.
ISNAD Naghiyev, Murad vd. “Bağımlılık Yapıcı Madde Kullanımı Riskinin Makine Öğrenmesi Yöntemleriyle Tahmini”. Abant Sağlık Bilimleri ve Teknolojileri Dergisi 3/2 (Ağustos 2023), 1-13.
JAMA Naghiyev M, Çetin Taş İ, Tekin Y. Bağımlılık Yapıcı Madde Kullanımı Riskinin Makine Öğrenmesi Yöntemleriyle Tahmini. SABİTED. 2023;3:1–13.
MLA Naghiyev, Murad vd. “Bağımlılık Yapıcı Madde Kullanımı Riskinin Makine Öğrenmesi Yöntemleriyle Tahmini”. Abant Sağlık Bilimleri Ve Teknolojileri Dergisi, c. 3, sy. 2, 2023, ss. 1-13.
Vancouver Naghiyev M, Çetin Taş İ, Tekin Y. Bağımlılık Yapıcı Madde Kullanımı Riskinin Makine Öğrenmesi Yöntemleriyle Tahmini. SABİTED. 2023;3(2):1-13.
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