Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence
| Autor: | Gin-Den Chen, Chi-Chang Chang, Chalong Cheewakriangkrai, Chih-Jen Tseng, Chi-Jie Lu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Support Vector Machine Psychological intervention Medicine (miscellaneous) computer.software_genre 03 medical and health sciences 0302 clinical medicine Risk Factors Artificial Intelligence medicine Data Mining Humans Extreme learning machine Ovarian Neoplasms Multivariate adaptive regression splines business.industry Medical record Cancer medicine.disease Ensemble learning Random forest 030104 developmental biology 030220 oncology & carcinogenesis Female Data mining Neoplasm Recurrence Local Ovarian cancer business computer |
| Zdroj: | Artificial Intelligence in Medicine. 78:47-54 |
| ISSN: | 0933-3657 |
| DOI: | 10.1016/j.artmed.2017.06.003 |
| Popis: | Ovarian cancer is the second leading cause of deaths among gynecologic cancers in the world. Approximately 90% of women with ovarian cancer reported having symptoms long before a diagnosis was made. Literature shows that recurrence should be predicted with regard to their personal risk factors and the clinical symptoms of this devastating cancer. In this study, ensemble learning and five data mining approaches, including support vector machine (SVM), C5.0, extreme learning machine (ELM), multivariate adaptive regression splines (MARS), and random forest (RF), were integrated to rank the importance of risk factors and diagnose the recurrence of ovarian cancer. The medical records and pathologic status were extracted from the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrated that the integrated C5.0 model is a superior approach in predicting the recurrence of ovarian cancer. Moreover, the classification accuracies of C5.0, ELM, MARS, RF, and SVM indeed increased after using the selected important risk factors as predictors. Our findings suggest that The International Federation of Gynecology and Obstetrics (FIGO), Pathologic M, Age, and Pathologic T were the four most critical risk factors for ovarian cancer recurrence. In summary, the above information can support the important influence of personality and clinical symptom representations on all phases of guide interventions, with the complexities of multiple symptoms associated with ovarian cancer in all phases of the recurrent trajectory. |
| Databáze: | OpenAIRE |
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