A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software
| Autor: | Alma Farnood, Mohammad Mehdi Emam, Ali Bahari, Nosratollah Naderi, Marjan Rashidi, Sattar Hashemi, Mohammad Reza Zali, Mohammad Reza Kangavari, Hamid Mohaghegh Shalmani, Naser Ebrahimi Daryani, Farzad Firouzi |
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| Rok vydání: | 2007 |
| Předmět: |
Adult
Male medicine.medical_specialty Adolescent Bone density Decision tree Disease Inflammatory bowel disease Absorptiometry Photon Crohn Disease Bone Density Risk Factors Internal medicine medicine Vitamin D and neurology Humans Diagnosis Computer-Assisted Aged Bone mineral Hepatology business.industry Patient Selection Decision Trees Smoking Gastroenterology Middle Aged Inflammatory Bowel Diseases medicine.disease Ulcerative colitis Surgery Osteoporosis Colitis Ulcerative Female business Densitometry Algorithms Software |
| Zdroj: | European Journal of Gastroenterology & Hepatology. 19:1075-1081 |
| ISSN: | 0954-691X |
| DOI: | 10.1097/meg.0b013e3282202bb8 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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