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A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software.
Eur J Gastroenterol Hepatol. 2007 Dec; 19(12):1075-81.EJ

Abstract

BACKGROUND

Decision tree classification is a standard machine learning technique that has been used for a wide range of applications. Patients with inflammatory bowel disease (IBD) are at increased risk of developing low bone mineral density (BMD). This study aimed at developing a new approach to select truly affected IBD patients who are indicated for densitometry, hence, subjecting fewer patients for bone densitometry and reducing expenses.

MATERIALS AND METHODS

Simple decision trees have been developed by means of WEKA (Waikato Environment for Knowledge Analysis) package of machine learning algorithms to predict factors influencing the bone density among IBD patients. The BMD status was the outcome variable whereas age, sex, duration of disease, smoking status, corticosteroid use, oral contraceptive use, calcium or vitamin D supplementation, menstruation, milk abstinence, BMI, and levels of calcium, phosphorous, alkaline phosphatase, and 25-OH vitamin D were all attributes.

RESULTS

Testing showed the decision trees to have sensitivities of 65.7-82.8%, specificities of 95.2-96.3%, accuracies of 86.2-89.8%, and Matthews correlation coefficients of 0.68-0.79. Smoking status was the most significant node (root) for ulcerative colitis and IBD-associated trees whereas calcium status was the root of Crohn's disease patients' decision tree.

CONCLUSION

BD specialists could use such decision trees to reduce substantially the number of patients referred for bone densitometry and potentially save resources.

Authors+Show Affiliations

Department of Inflammatory Bowel Disease, Research Center for Gastroenterology and Liver Diseases, Shaheed Beheshti University of Medical Sciences, Tehran, Iran. firouzifar1975@yahoo.comNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

17998832

Citation

Firouzi, Farzad, et al. "A Decision Tree-based Approach for Determining Low Bone Mineral Density in Inflammatory Bowel Disease Using WEKA Software." European Journal of Gastroenterology & Hepatology, vol. 19, no. 12, 2007, pp. 1075-81.
Firouzi F, Rashidi M, Hashemi S, et al. A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software. Eur J Gastroenterol Hepatol. 2007;19(12):1075-81.
Firouzi, F., Rashidi, M., Hashemi, S., Kangavari, M., Bahari, A., Daryani, N. E., Emam, M. M., Naderi, N., Shalmani, H. M., Farnood, A., & Zali, M. (2007). A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software. European Journal of Gastroenterology & Hepatology, 19(12), 1075-81.
Firouzi F, et al. A Decision Tree-based Approach for Determining Low Bone Mineral Density in Inflammatory Bowel Disease Using WEKA Software. Eur J Gastroenterol Hepatol. 2007;19(12):1075-81. PubMed PMID: 17998832.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software. AU - Firouzi,Farzad, AU - Rashidi,Marjan, AU - Hashemi,Sattar, AU - Kangavari,Mohammadreza, AU - Bahari,Ali, AU - Daryani,Naser Ebrahimi, AU - Emam,Mohammad Mehdi, AU - Naderi,Nosratollah, AU - Shalmani,Hamid Mohaghegh, AU - Farnood,Alma, AU - Zali,Mohammadreza, PY - 2007/11/14/pubmed PY - 2008/2/29/medline PY - 2007/11/14/entrez SP - 1075 EP - 81 JF - European journal of gastroenterology & hepatology JO - Eur J Gastroenterol Hepatol VL - 19 IS - 12 N2 - BACKGROUND: Decision tree classification is a standard machine learning technique that has been used for a wide range of applications. Patients with inflammatory bowel disease (IBD) are at increased risk of developing low bone mineral density (BMD). This study aimed at developing a new approach to select truly affected IBD patients who are indicated for densitometry, hence, subjecting fewer patients for bone densitometry and reducing expenses. MATERIALS AND METHODS: Simple decision trees have been developed by means of WEKA (Waikato Environment for Knowledge Analysis) package of machine learning algorithms to predict factors influencing the bone density among IBD patients. The BMD status was the outcome variable whereas age, sex, duration of disease, smoking status, corticosteroid use, oral contraceptive use, calcium or vitamin D supplementation, menstruation, milk abstinence, BMI, and levels of calcium, phosphorous, alkaline phosphatase, and 25-OH vitamin D were all attributes. RESULTS: Testing showed the decision trees to have sensitivities of 65.7-82.8%, specificities of 95.2-96.3%, accuracies of 86.2-89.8%, and Matthews correlation coefficients of 0.68-0.79. Smoking status was the most significant node (root) for ulcerative colitis and IBD-associated trees whereas calcium status was the root of Crohn's disease patients' decision tree. CONCLUSION: BD specialists could use such decision trees to reduce substantially the number of patients referred for bone densitometry and potentially save resources. SN - 0954-691X UR - https://www.unboundmedicine.com/medline/citation/17998832/A_decision_tree_based_approach_for_determining_low_bone_mineral_density_in_inflammatory_bowel_disease_using_WEKA_software_ L2 - https://doi.org/10.1097/MEG.0b013e3282202bb8 DB - PRIME DP - Unbound Medicine ER -