Purpose
Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods. These methods provide a tool for predicting ovarian cancer according to the characteristics and conditions of each person.
Method
In this study, a dataset included records related to 171 cases of benign ovarian tumors and 178 records related to cases of ovarian cancer were analyzed. The dataset contains the records of blood test results and tumor markers of the patients. After data pre-processing, including removing outliers and replacing missing values, the weight of the effective factors was determined using information gain indices and the Gini index. In the next step, predictive models were created using Decision Trees, Support Vector Machine, Random Forest, and Artificial Neural Network models. The performance of these models was evaluated using the 10-fold cross-validation method using the indicators of accuracy, sensitivity, specificity, and the area under the ROC (Receiver operating characteristic) curve. Finally, by comparing the performance of the models, the best predictive model of ovarian cancer was selected.
Results
The most important predictive factors were HE4, CA125, and NEU. The Random Forest model was identified as the best predictive model with an accuracy of more than 86%. The predictive accuracy of Decision Tree, Support Vector Machine, and Artificial Neural Network models was estimated as 82.91%, 85.25%, and 79.35%, respectively. Various AI tools can be used with high accuracy and sensitivity in predicting ovarian cancer.
Conclusion
Therefore, the use of these tools can help specialists and patients with early, easier, and less expensive diagnosis of ovarian cancer. Future research can use AI by combining image data with serum biological indicators to develop new models and promote the diagnosis and treatment of ovarian cancer.