Acta Scientific Cancer Biology (ASCB)

Research Article Volume 4 Issue 3

Prediction of Attendance to the "Law of 60 Days" in Breast Cancer Patients using Machine Learning Classifiers

Sandra Gioia1,3,5*, Renata Galdino2, Lucia Brigagão2, Antonio Valadares3, Fernando Secol4, Sandra San Miguel4, Alexandra Bukowski5, Lindsay Krush5 and Paul Goss5

1Brazilian National Cancer Institute (INCA), Rio De Janeiro, Brazil
2State Secretariat Of Health, Rio De Janeiro, Brazil
3Israelita Albert Einstein Hospital, São Paulo, Brazil
4National Cancer Institute, Washington DC, USA
5Global Cancer Institute, Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, Massachusetts, USA

*Corresponding Author: Sandra Gioia, Brazilian National Cancer Institute (INCA), Rio De Janeiro, Brazil

Received: January 17, 2020; Published: February 11, 2020

×

Abstract

  An applied study was conducted on how the use of machine learning techniques can help in the process of identifying compliance with the "60 Day Law", which states that all patients with cancer within the public system must initiate the treatment within 60 days after the diagnosis of cancer. Within the Patient Navigation Program (PNP) for breast cancer in Rio de Janeiro, the study aims to construct a model that accurately predicts whether or not a patient meets the period established in the Law. From August 2017 to May 2018, 105 patients aged 33 - 80 years (mean 59 years) were recruited for navigation. Patient Navigator (NP) applied questionnaires to collect clinical, psychosocial, and patient satisfaction information. The follow-up was by phone, email, or text message. For the development of the statistical analysis, three learning models were used: AdaBoost, Decision Tree and Gaussian NB. AdaBoost learning model had superior results in relation to accuracy and f-score (0.8889 and 0.8333, respectively) and with good performance in relation to the prediction times. We identified 38 important attributes that contribute 95% of the importance of all the attributes present in the data. We identified 38 important attributes for compliance with the Law, which simplifies the information required for model learning. In the Brazilian context, the PNP may represent an opportunity to adequately implement existing legislation and, as such, would have great potential for integration into federal, state, and local health systems.

Keywords: Patient Navigation; Breast Neoplasms; Barriers; Health Systems; Machine Learning

×

References

  1. Bray F., et al. “Global estimates of cancer prevalence for 27 sites in the adult population in 2008”. International Journal of Cancer 132.5 (2013):1133-1145.
  2. Goss PE., et al. “Planning cancer control in Latin America and the Caribbean”. The Lancet Oncology 14 (2013): 391-436.
  3. Bray F., et al. “Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries”. CA: A Cancer Journal for Clinicians (2018): 1-31.
  4. Instituto Nacional de Câncer (Brasil): Estatísticas do câncer: Mortalidade. 
  5. Instituto Nacional de Câncer (Brasil): Estimativa  Incidência de câncer no Brasil (2018). 
  6. Unger-Saldãna K. “Challenges to the early diagnosis and treatment of breast cancer in developingcountries”. World Journal of Clinical Oncology 5 (2014): 465-477.
  7. Brasil. Presidência da República. Lei no. 12.732, de 22 de novembro de 2012. Dispõe sobre o primeiro tratamento de paciente com neoplasia maligna comprovada e estabelece prazo para seu início”. Diário Oficial da União 23 nov. Seção I (2012): 1.
  8. Ministério da Saúde. Portaria nº 3.394, de 30 de dezembro de Institui o Sistema de Informação de Câncer (SICAN) no âmbito do Sistema Único de Saúde (SUS) ”. Diário Oficial da União 31 dez. Seção I (2013): 57-8.
  9. Federação Brasileira de Instituições Filantrópicas de Apoio a Saúde da Mama: Pesquisa: A implementação da Lei dos 60 dias. 
  10. Bukowski A., et al. “The potential role of patient navigation in low- and middle-income countries for patients with cancer”. JAMA Oncology 2 (2016): 994-995.
  11. Bukowski A., et al. “Patient Navigation to Improve Access to Breast Cancer Care in Brazil”. Journal of Global Oncology 5 (2017): 433-437. 
  12. Lantz B. “Machine Learning with R”. Packt Publishing (2014).
  13. Harford J., et al. “Guideline Implementation for Breast Healthcare in Low- and Middle-Income Countries”. CANCER Supplement 15.113 (2008): 2282-2296.
  14. Gioia S. “Why is breast cancer early detection important?” Mastology 27.3 (2017): 173-175.
  15. Naomi Ko., et al. “Can Patient Navigation Improve Receipt of Recommended Breast Cancer Care? Evidence from the National Patient Navigation Research Program”. Journal of Clinical Oncology 32 (2014): 2758-2764.
×

Citation

Citation: Sandra Gioia., et al. “Prediction of Attendance to the "Law of 60 Days" in Breast Cancer Patients using Machine Learning Classifiers”.Acta Scientific Cancer Biology 4.3 (2020): 01-13.



Member In



News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for March Issue
    The last date for submission of articles for regular Issues is April 15, 2020.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of “Best Article of the Issue”.
  • Welcoming Article Submission
    Acta Scientific delightfully welcomes active researchers for submission of articles towards the upcoming issue of respective journals.
  • Contact US