The Discovery of Oral Cancer Prognostic Factor Ranking Using Association Rule Mining

被引:0
|
作者
Chaowchuen, Sitthi [1 ]
Warin, Kritsasith [2 ,5 ]
Somyanonthanakul, Rachasak [3 ]
Panichkitkosolkul, Wararit [4 ]
Suebnukarn, Siriwan [2 ]
机构
[1] Udonthani Canc Hosp, Muang Udonthani, Udonthani, Thailand
[2] Thammasat Univ, Fac Dent, Pathum Thani, Thailand
[3] Rangsit Univ, Coll Digital Innovat Technol, Pathum Thani, Thailand
[4] Thammasat Univ, Fac Sci & Technol, Pathum Thani, Thailand
[5] Thammasat Univ, Fac Dent, Divisionof Oral & Maxillofacial Surg, Pathum Thani 12121, Thailand
关键词
oral cancer; prognostic factors; survival rate; data mining; association rule mining; SURVIVAL; EPIDEMIOLOGY; MANAGEMENT; CARCINOMA;
D O I
10.1055/s-0043-1777050
中图分类号
R78 [口腔科学];
学科分类号
1003 ;
摘要
Objective A 5-year survival rate is a predictor for the assessment of oral cancer prognosis. The purpose of this study is to analyze oral cancer data to discover and rank the prognostic factors associated with oral cancer 5-year survival using the association rule mining (ARM) technique. Materials and Methods This study is a retrospective analysis of 897 oral cancer patients from a regional cancer center between 2011 and 2017. The 5-year survival rate was assessed. The multivariable Cox proportional hazards analysis was performed to determine prognostic factors. ARM was applied to clinicopathologic and treatment modalities data to identify and rank the prognostic factors associated with oral cancer 5-year survival. Results The 5-year overall survival rate was 35.1%. Multivariable Cox proportional hazards analysis showed that tumor (T) stage, lymph node metastasis, surgical margin, extranodal extension, recurrence, and distant metastasis of tumor were significantly associated with overall survival rate (p < 0.05). The top associated death within 5 years rule was positive extranodal extension, followed by positive perineural and lymphovascular invasion, with confidence levels of 0.808, 0.808, and 0.804, respectively. Conclusion This study has shown that extranodal extension, and perineural and lymphovascular invasion were the top ranking and major deadly prognostic factors affecting the 5-year survival of oral cancer.
引用
收藏
页码:907 / 917
页数:11
相关论文
共 50 条
  • [21] Significant patterns for oral cancer detection: association rule on clinical examination and history data
    Neha Sharma
    Hari Om
    Network Modeling Analysis in Health Informatics and Bioinformatics, 2014, 3 (1)
  • [22] Implementation of Association Rule Mining using CUDA
    Adil, Syed Hasan
    Qamar, Sadaf
    ICET: 2009 INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES, PROCEEDINGS, 2009, : 332 - +
  • [23] Using association rule mining for the QSAR problem
    Dumitriu, L.
    Craciun, M-V.
    Segal, C.
    Cocu, A.
    Georgescu, L. P.
    2006 3RD INTERNATIONAL IEEE CONFERENCE INTELLIGENT SYSTEMS, VOLS 1 AND 2, 2006, : 519 - 522
  • [24] The Improvement of SOME/IP Service Discovery via Association Rule Mining
    Saydam, Berkay
    2022 9TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ICEEE 2022), 2022, : 285 - 289
  • [25] News Relation Discovery Based on Association Rule Mining with Combining Factors
    Kittiphattanabawon, Nichnan
    Theeramunkong, Thanaruk
    Nantajeewarawat, Ekawit
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2011, E94D (03): : 404 - 415
  • [26] Fast Top-K association rule mining using rule generation property pruning
    Liu, Xiangyu
    Niu, Xinzheng
    Fournier-Viger, Philippe
    APPLIED INTELLIGENCE, 2021, 51 (04) : 2077 - 2093
  • [27] Fast Top-K association rule mining using rule generation property pruning
    Xiangyu Liu
    Xinzheng Niu
    Philippe Fournier-Viger
    Applied Intelligence, 2021, 51 : 2077 - 2093
  • [28] Discovery and ranking of the most robust prognostic biomarkers in serous ovarian cancer
    Balázs Győrffy
    GeroScience, 2023, 45 : 1889 - 1898
  • [29] An effective association rule mining scheme using a new generic basis
    Sahoo, Jayakrushna
    Das, Ashok Kumar
    Goswami, A.
    KNOWLEDGE AND INFORMATION SYSTEMS, 2015, 43 (01) : 127 - 156
  • [30] Detecting software design defects using relational association rule mining
    Gabriela Czibula
    Zsuzsanna Marian
    Istvan Gergely Czibula
    Knowledge and Information Systems, 2015, 42 : 545 - 577