An Evaluation of Machine Learning Algorithms and Feature Selection Methods for Cervical Cancer Risk Prediction using Clinical Features

Authors

  • Sokaina El Khamlıchı Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco https://orcid.org/0000-0001-8848-9661
  • Ikram Ben Abdel Ouahab Computer science, systems and telecommunication laboratory (LIST), Faculty of sciences and techniques, University Abdelmalek Essaadi, Tangier, Morocco https://orcid.org/0000-0003-0955-6382
  • Mohammed Bouhorma Computer science, systems and telecommunication laboratory (LIST), Faculty of sciences and techniques, University Abdelmalek Essaadi, Tangier, Morocco https://orcid.org/0000-0002-5687-5231
  • Fatiha Elouaaı Computer science, systems and telecommunication laboratory (LIST), Faculty of sciences and techniques, University Abdelmalek Essaadi, Tangier, Morocco https://orcid.org/0000-0002-7139-5682
  • Abdelfettah Sedquı Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco https://orcid.org/0000-0002-7446-0400
  • Amal Maurady Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco https://orcid.org/0000-0001-9298-717X

Keywords:

Cervical Cancer, Decision Tree, Feature Selection, RFE, SMOTE, Supervised Machine Learning

Abstract

Cervical cancer is one of the most frequent gynecological cancers worldwide. It is associated to several risk factors like sexually transmitted diseases, human papillomavirus and smoking. The early diagnosis of this disease is crucial to lower fatality rates. Furthermore, its early prediction can support clinicians and patients to have an effective treatment. This study intends to compare machine learning classifiers to determine the best model to predict cervical cancer and identify its most significant risk factors. This work compares five machine learning algorithms: K-Nearest Neighbor, Gaussian Naïve Bayes, Logistic Regression, Random Forest and Decision Tree (DT). Afterwards, the study continues to enhance the outcome of DT algorithm through balancing the data with Synthetic Minority Oversampling Technique (SMOTE), selecting the most important features with Recursive Feature Elimination (RFE) and tuning hyperparameters with Grid Search technique. Overall, the combination of Decision Tree classification technique with SMOTE and tuning hyperparameters with Grid Search method presents the most performing model.

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Flowchart of the proposed solution

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Published

16.12.2022

How to Cite

Khamlıchı, S. E. ., Ouahab, I. B. A. ., Bouhorma, M. ., Elouaaı, F. ., Sedquı, A. ., & Maurady, A. . (2022). An Evaluation of Machine Learning Algorithms and Feature Selection Methods for Cervical Cancer Risk Prediction using Clinical Features. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 470–479. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2284

Issue

Vol. 10 No. 4 (2022)

Section

Research Article

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