Optimised Feature Selection and Cervical Cancer Prediction Using Machine Learning Classification

Cervical Cancer Prediction

  • Amit Tak Project Scientist C (Medical)
  • Puran Mal Parihar Associate Professor
  • Dharmendra Singh Fatehpuriya Associate Professor
  • Yogesh Singh Associate Professor
Keywords: Biopsy,, Cervical cancer, Cytology, Hinselmann, Machine learning, Schiller

Abstract


Background/Aim: Screening and early detection play a key role in cervical cancer prevention. The present study predicts the outcome of various diagnostic tests used to diagnose cervical cancer using machine learning algorithms.

Methods: The present study ran various cervical cancer risk factors on a machine learning (ML) classifier to predict outcomes of Hinselmann, Schiller, cytology and biopsy. The dataset is publicly available on the Machine Learning Repository website of the University of California Irvine. The imbalanced dataset was pre-processed using oversampling methods. The significantly varied features between the two levels of a response variable were used to train the machine learning classifiers on MATLAB. The classifiers used were Decision Trees, Support Vector Machine, K-Nearest Neighbours and Ensemble learning classifiers. The performance metrics of the classifiers were expressed as accuracy, the area under the receiver operator characteristic (AU-ROC) curve, sensitivity and specificity.

Results: The Fine Gaussian SVM classifier was the best to classify Hinselmann, cytology and biopsy with the accuracy of 97.5 %, 62.5 % and 98 %, respectively. However, Boosted trees performed best in the classification of Schiller with 81.3 % accuracy.

 

Conclusion: The present study selected optimised features among multiple risk factors to train various ML classifiers to predict cervical cancer.

 

Author Biographies

Puran Mal Parihar, Associate Professor

Geetanjali Medical College, Udaipur, India

Dharmendra Singh Fatehpuriya, Associate Professor

JLN Medical College, Ajmer, Rajasthan, India

Yogesh Singh, Associate Professor

Zoram Medical College, Falkawn, Mizoram, India

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Published
2022/09/30
Section
Original article