Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence

Tseng, Chih-Jen; Lu, Chi-Jie; Chang, Chi-Chang; Chen, Gin-Den; Cheewakriangkrai, Chalong

doi:10.1016/j.artmed.2017.06.003

Cited by 76Artificial Intelligence in HealthcareAI in cancer detectionOvarian cancer diagnosis and treatment

Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence

Tseng, Chih-Jen, Lu, Chi-Jie, Chang, Chi-Chang, Chen, Gin-Den, Cheewakriangkrai, Chalong·2017·Artificial Intelligence in Medicine

DOI 10.1016/j.artmed.2017.06.003

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Abstract

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.

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Authors: Tseng, C.-J., Lu, C.-J., Chang, C.-C., Chen, G.-D., & Cheewakriangkrai, C.
Affiliations: Chung Shan Medical University Hospital (TW)
Chien Hsin University of Science and Technology (TW)
Chiang Mai University (TH)
Corresponding author: Chang, Chi-Chang
ORCID: Tseng, Chih-Jen
Lu, Chi-Jie
Chang, Chi-Chang
Chen, Gin-Den
Cheewakriangkrai, Chalong
Venue: Artificial Intelligence in Medicine
Publisher: Elsevier BV
Published: 2017-05
Volume: 78
Pages: 47-54
ISSN: 0933-3657 (ISSN-L)
DOI: 10.1016/j.artmed.2017.06.003
OpenAlex: W2623169439
Cited by: 76
References: 44
Citations by year: 2026: 4 · 2025: 2 · 2024: 5 · 2023: 11 · 2022: 12 · 2021: 17 · 2020: 13 · 2019: 6 · 2018: 5 · 2017: 1
FWCI: 9.41
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Artificial Intelligence in Medicine (publishedVersion)
PubMed (publishedVersion)
Funders: Chung Shan Medical University Hospital (CSH-2014-C-018)
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Reproductive Medicine › Ovarian cancer diagnosis and treatment
Keywords: ovarian cancer, tumor recurrence prediction, risk factors, ensemble learning, C5.0, support vector machine, extreme learning machine, multivariate adaptive regression splines, random forest, feature importance ranking, FIGO staging, pathological stage, cancer registry data
MeSH terms: Female, Humans, Neoplasm Recurrence, Local, Ovarian Neoplasms, Risk Factors, Data Mining, Support Vector Machine

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Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence

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