Optimizovan izbor karakteristika i predviđanje raka grlića materice korišćenjem klasifikacije mašinskog učenja
Cervical Cancer Prediction
Sažetak
Background: Screening and early detection of cervical cancer have a key role in prevention. The present study uses machine learning algorithms to predict the outcome of various diagnostic tests used to diagnose cervical cancer.
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 different features between the two levels of a response variable were used for training on the MATLAB Classifier application. The classifiers used were Decision Trees, Support Vector Machine, K-Nearest Neighbors, and Ensemble learning classifiers. The performance metrics of the machine learning classifier 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 model 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 optimized features among multiple risk factors to train various ML classifiers to predict cervical cancer.
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