Novel solutions for an old disease: Diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks
| Autor: | Min-Huei Hsu, Wen Ta Chiu, Nai Hsin Lee, Yu-Chuan Li, Ruey Hwa Lu, Chung Ho Hsieh |
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| Rok vydání: | 2011 |
| Předmět: |
Adult
Male Databases Factual Taiwan Decision tree Logistic regression Sensitivity and Specificity Artificial Intelligence Statistics Appendectomy Humans Medicine Diagnosis Computer-Assisted Artificial neural network Receiver operating characteristic business.industry Decision Trees Middle Aged Appendicitis medicine.disease Random forest Support vector machine Logistic Models Alvarado score Acute Disease Female Surgery Neural Networks Computer business Algorithms |
| Zdroj: | Surgery. 149:87-93 |
| ISSN: | 0039-6060 |
| DOI: | 10.1016/j.surg.2010.03.023 |
| Popis: | Background Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis. Methods Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score. Results Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16–85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado. Conclusion We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making. |
| Databáze: | OpenAIRE |
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