Decision Tree-Based Classification Approach to Discover Factors Affecting Vitamin D Level with Machine Learning

Yıl 2024, Cilt: 8 Sayı: 2, 336 - 348, 31.05.2024

Ceyda Ünal , Cihan Çılgın , Süleyman Albaş , Esra Meltem Koç

https://doi.org/10.30621/jbachs.1284274

Öz

Decision Tree-Based Classification Approach to Discover Factors Affecting Vitamin D Level with Machine Learning

Öz

Purpose: Vitamin D level is emphasized as an important biomarker in determining risk factors for different diseases. Vitamin D is an important vitamin for human health and its deficiency is associated with serious health problems. Therefore, it is of great importance to detect vitamin D deficiency, which can be easily prevented and treated. The possible relationship between vitamin D deficiency and musculoskeletal pain, osteoporosis, diabetes mellitus, hypertension is frequently discussed in researches. In this research, it is aimed to analyze the factors in determining the vitamin D level and the decision rules related to it.
Methods: A descriptive framework based on one of the machine learning techniques, that is decision tree is followed. The data used to create the decision rules were obtained from volunteers between the ages of 18-85 who applied to Izmir Katip Çelebi University Atatürk Training and Research Hospital Infectious Diseases and Family Medicine Polyclinics and agreed to participate in the study between 01.03.2017 and 01.09.2017.
Results: It was observed that age, gender and laboratory test values are strong predictors for vitamin D level. As a result of two CART (Classification and Regression Trees) models, %90.47 and %95 predictive accuracies were observed respectively. In the first model, uric acid, age and creatine; in the second model TSH, ALP and smoking(yes) were the most important three biomarkers affecting vitamin D level.
Discussion: The collected features give a comprehensive list of variables that have an effect on vitamin D in the dataset under consideration. Important findings of the study include not only the identification of these variables, but also the effective categorization determination procedures. In contrast to previous research, the Age variable is the most influential factor within the scope of this dataset, which includes demographic information on patients and their existing disorders.

Anahtar Kelimeler

machine learning, decision trees, decision rules, vitamin D.

Kaynakça

• Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients. Health Aff (Millwood). 2014 Jul 1;33(7):1123–31.
• Cui A, Zhang T, Xiao P, Fan Z, Wang H, Zhuang Y. Global and regional prevalence of vitamin D deficiency in population-based studies from 2000 to 2022: A pooled analysis of 7.9 million participants. Front Nutr. 2023;10:1070808.
• The vitamin D deficiency pandemic: Approaches for diagnosis, treatment and prevention - PubMed [Internet]. [cited 2024 Feb 25]. Available from: https://pubmed.ncbi.nlm.nih.gov/28516265/
• Bhan I, Burnett-Bowie SAM, Ye J, Tonelli M, Thadhani R. Clinical measures identify vitamin D deficiency in dialysis. Clin J Am Soc Nephrol CJASN. 2010 Mar;5(3):460–7.
• Gonoodi K, Tayefi M, Saberi-Karimian M, Amirabadi zadeh A, Darroudi S, Farahmand SK, et al. An assessment of the risk factors for vitamin D deficiency using a decision tree model. Diabetes Metab Syndr Clin Res Rev. 2019 May 1;13(3):1773–7.
• Snapshot [Internet]. [cited 2021 May 20]. Available from: https://www.semanticscholar.org/paper/Analysis-of-the-Association-Between-Vitamin-D-and-Kaya-G%C3%BCnay/afa2b3f946f889d94ef8ecff60566d174690ec71
• Kim C, Lee SH, Lim JS, Kim Y, Jang MU, Oh MS, et al. Impact of 25-Hydroxyvitamin D on the Prognosis of Acute Ischemic Stroke: Machine Learning Approach. Front Neurol [Internet]. 2020 [cited 2023 Apr 12];11. Available from: https://www.frontiersin.org/articles/10.3389/fneur.2020.00037
• Osmani F, Ziaee M. Assessment of the risk factors for vitamin D3 deficiency in chronic hepatitis B patient using the decision tree learning algorithm in Birjand. Inform Med Unlocked. 2021 Jan 1;23:100519.
• Rahimi Z, Abdolvand N, Sepehri MM, Khavanin Zadeh M. The association of vitamin-D level with catheter-related-thrombosis in hemodialysis patients: A data mining model. J Vasc Access. 2021 Mar 14;11297298211001156.
• Osteoporosis and Metabolic Bone Diseases Diagnosis and Treatment Guide. Turk J Endocrinol Metab [Internet]. 2017 [cited 2024 Feb 25]; Available from: http://www.temd.org.tr/files/OSTEOPOROZ_web.pdf
• Kira K, Rendell LA. A Practical Approach to Feature Selection. In: Sleeman D, Edwards P, editors. Machine Learning Proceedings 1992 [Internet]. San Francisco (CA): Morgan Kaufmann; 1992 [cited 2021 May 21]. p. 249–56. Available from: https://www.sciencedirect.com/science/article/pii/B9781558602472500371
• Guyon I, Elisseeff A. An Introduction to Variable and Feature Selection. undefined [Internet]. 2003 [cited 2021 May 21]; Available from: /paper/An-Introduction-to-Variable-and-Feature-Selection-Guyon-Elisseeff/d8384f7ef288d2d5cb267128471c5427fc98b54b
• Ratanamahatana C “ann”, Gunopulos D. Feature selection for the naive bayesian classifier using decision trees. Appl Artif Intell. 2003 May 1;17(5–6):475–87.
• Sugumaran V, Muralidharan V, Ramachandran KI. Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing. Mech Syst Signal Process. 2007 Feb 1;21(2):930–42.
• Delen D, Kuzey C, Uyar A. Measuring firm performance using financial ratios: A decision tree approach. Expert Syst Appl. 2013 Aug 1;40(10):3970–83.
• Liu C, Hu Z, Li Y, Liu S. Forecasting copper prices by decision tree learning. Resour Policy. 2017 Jun 1;52:427–34.
• Agarwal S, Pandey GN, Tiwari M. Data Mining in Education: Data Classification and Decision Tree Approach. undefined [Internet]. 2012 [cited 2021 May 24]; Available from: /paper/Data-Mining-in-Education%3A-Data-Classification-and-Agarwal-Pandey/cbae094b050780f141632e18499b2fcf309c3687
• Kolo KD, Adepoju SA, Alhassan J. A Decision Tree Approach for Predicting Students Academic Performance. undefined. 2015;
• Fan GZ, Ong SE, Koh HC. Determinants of House Price: A Decision Tree Approach. Urban Stud. 2006 Nov 1;43(12):2301–15.
• Shinde N, Gawande K. Survey on predicting property price. In: 2018 International Conference on Automation and Computational Engineering (ICACE). 2018. p. 1–7.
• Li X, Chan CW, Nguyen HH. Application of the Neural Decision Tree approach for prediction of petroleum production. J Pet Sci Eng. 2013 Apr 1;104:11–6.
• Mikučionienė R, Martinaitis V, Keras E. Evaluation of energy efficiency measures sustainability by decision tree method. Energy Build. 2014 Jun 1;76:64–71.
• Razavi AR, Gill H, Ahlfeldt H, Shahsavar N. Predicting metastasis in breast cancer: comparing a decision tree with domain experts. J Med Syst. 2007 Aug;31(4):263–73.
• Chang CL, Chen CH. Applying decision tree and neural network to increase quality of dermatologic diagnosis. Expert Syst Appl. 2009 Mar 1;36(2, Part 2):4035–41.
• Bayat S, Cuggia M, Rossille D, Kessler M, Frimat L. Comparison of Bayesian network and decision tree methods for predicting access to the renal transplant waiting list. Stud Health Technol Inform. 2009;150:600–4.
• Chaurasia V, Pal S, Tiwari BB. Chronic Kidney Disease: A Predictive model using Decision Tree. Chronic Kidney Dis. :14.
• Singh D, Choudhary N, Samota J. Analysis of Data Mining Classification with Decision treeTechnique. 2013;7.
• Koh HC, Tan G. Data mining applications in healthcare. J Healthc Inf Manag JHIM. 2005;19(2):64–72.
• Gandomi AH, Fridline MM, Roke DA. Decision Tree Approach for Soil Liquefaction Assessment. Sci World J. 2013 Dec 30;2013:e346285.
• Kurt_Cilgin_The.pdf [Internet]. [cited 2021 May 24]. Available from: https://teacongress.com/papers/Kurt_Cilgin_The.pdf
• Safavian SR, Landgrebe D. A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern. 1991 May;21(3):660–74.
• Aggarwal CC. Data Mining: The Textbook [Internet]. Springer International Publishing; 2015 [cited 2021 May 24]. Available from: https://www.springer.com/gp/book/9783319141411
• Singh S, Gupta P. Comparative Study Id3, Cart and C4.5 Decision Tree Algorithm: A Survey.
• Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and Regression Trees. Taylor & Francis; 1984. 372 p.
• Sarkar S, Patel A, Madaan S, Maiti J. Prediction of occupational accidents using decision tree approach. In: 2016 IEEE Annual India Conference (INDICON). 2016. p. 1–6.
• Waheed T, Bonnell RB, Prasher SO, Paulet E. Measuring performance in precision agriculture: CART—A decision tree approach. Agric Water Manag. 2006 Jul 16;84(1):173–85.
• Hoffmann G, Bietenbeck A, Lichtinghagen R, Klawonn F. Using machine learning techniques to generate laboratory diagnostic pathways—a case study. J Lab Precis Med [Internet]. 2018 Jun 29 [cited 2023 Apr 12];3(6). Available from: https://jlpm.amegroups.com/article/view/4401