مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

video

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

sound

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Version

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

View:

202
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Download:

190
مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Cites:

Information Journal Paper

Title

Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer

Pages

  513-522

Abstract

 Background: Compared to other genital cancers, Cervical Cancer is the most prevalent and the main cause of mortality in females in third-world countries, affected by different factors, including smoking, poor nutritional status, immune-deficiency, long-term use of contraceptives and so on. Objective: The present study was conducted to predict Cervical Cancer and identify its important predictors using Machine Learning classification algorithms. Material and Methods: In a cross-sectional study, the data of 145 patients with 23 attributes, which referred to Shohada Hospital Tehran, Iran during 2017– 2018, were analyzed by Machine Learning classification algorithms which included SVM, QUEST, C&R tree, MLP and RBF. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, specificity and area under the curve (AUC). Results: The accuracy, sensitivity, specificity and AUC of Quest and C&R tree were, respectively 95. 55, 90. 48, 100, and 95. 20, 95. 55, 90. 48, 100, and 95. 20, those of RBF 95. 45, 90. 00, 100 and 91. 50, those of SVM 93. 33, 90. 48, 95. 83 and 95. 80 and those of MLP 90. 90, 90. 00, 91. 67 and 91. 50 percentage. The important predictors in all the algorithms were found to comprise personal health level, marital status, social status, the dose of contraceptives used, level of education and number of caesarean deliveries. Conclusion: This investigation confirmed that ML can enhance the Prediction of Cervical Cancer. The results of this study showed that Decision Tree algorithms can be applied to identify the most relevant predictors. Moreover, it seems that improving personal health and socio-cultural level of patients can be causing Cervical Cancer prevention.

Multimedia

  • No record.

    • Cites

  • No record.

    • References

  • No record.

    • Cite

    APA: Copy

    ASADI, F., SALEHNASAB, C., & AJORI, L.. (2020). Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer. JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING, 10(4), 513-522. SID. https://sid.ir/paper/334796/en

    Vancouver: Copy

    ASADI F., SALEHNASAB C., AJORI L.. Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer. JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING[Internet]. 2020;10(4):513-522. Available from: https://sid.ir/paper/334796/en

    IEEE: Copy

    F. ASADI, C. SALEHNASAB, and L. AJORI, “Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer,” JOURNAL OF BIOMEDICAL PHYSICS AND ENGINEERING, vol. 10, no. 4, pp. 513–522, 2020, [Online]. Available: https://sid.ir/paper/334796/en

    Related Journal Papers

  • No record.

    • Related Seminar Papers

  • No record.

    • Related Plans

  • No record.

    • Recommended Workshops






    Move to top
    telegram sharing button
    whatsapp sharing button
    linkedin sharing button
    twitter sharing button
    email sharing button
    email sharing button
    email sharing button
    sharethis sharing button