Cervical Cancer Diagnosis Using an Integrated System of Principal Component Analysis, Genetic Algorithm, and Multilayer Perceptron

Cervical cancer is one of the most dangerous diseases that affect women worldwide. The diagnosis of cervical cancer is challenging, costly, and time-consuming. Existing literature has focused on traditional machine learning techniques and deep learning to identify and predict cervical cancer. This research proposes an integrated system of Genetic Algorithm (GA), Multilayer Perceptron (MLP), and Principal Component Analysis (PCA) that accurately predicts cervical cancer. GA is used to optimize the MLP hyperparameters, and the MLPs act as simulators within the GA to provide the prediction accuracy of the solutions. The proposed method uses PCA to transform the available factors; the transformed features are subsequently used as inputs to the MLP for model training. To contrast with the PCA method, different subsets of the original factors are selected. The performance of the integrated system of PCA-GA-MLP is compared with nine different classification algorithms. The results indicate that the proposed method outperforms the studied classification algorithms. The PCA-GA-MLP model achieves the best accuracy in diagnosing Hinselmann, Biopsy, and Cytology when compared to existing approaches in the literature that were implemented on the same dataset. This study introduces a robust tool that allows medical teams to predict cervical cancer in its early stage.

Automated summary

This research proposes an integrated system of Genetic Algorithm, Multilayer Perceptron, and Principal Component Analysis that accurately predicts cervical cancer. By optimizing hyperparameters and transforming features, the proposed method outperforms other classification algorithms and can be used for early prediction of cervical cancer.