摘要：Objectives: To build decision trees to predict intrauterine disease, based on a clinical data set, and using mathematical software. Methods: Diagnostic algorithms were built and validated using the data of 402 consecutive patients who underwent grey scale ultrasound, followed by colou r Doppler, saline infusion sonograp hy (SIS), office hysteroscopy and endometrial sampling. The "final diagnosis" was classified as "abnormal" in case of endometrial polyps, hyperplasia or malignancy or intracavitary myoma. "Pre-test parameters" included patient's age, weight, length, parity, menopausal status, bleeding symptoms and cervical cytology; "post-test parameters" included ultrasound-, color Doppler-, SIS-, hysteroscopy- findings and histology results after endometrial sampling. Decision Tree #1 was built using both "pre- test" and "post-test" parameters; Tree #2 was only based on "post-test" parameters; Tree #3 was designed without using the hysteroscopy variables. The Waikato Environment for Knowledge Analysis (Weka) software was used for the development of decision trees. Results: All trees started with an imaging technique: hysteroscopy or SIS. The diagnostic accuracy was 88.3%, 88.3% and 84.0% for Tree #1, #2 and #3 respectively, the sensitivity and specificity was 95.5% and 82%, 97.7% and 80.0, 93.2 and 76.0%, respectively. Conclusion: The method used in this study enables the comparison between different decision trees containing multiple tests.
