Abstract
An experimental study is conducted to predict the malignant diagnosis of patients from 1989 to 1995 donated by Olvi Mangasarian, Computer Science Department, University of Wisconsin, WI. A secondary dataset from the UCI machine library is analyzed using Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifier with feature extraction applied by Principal Component Analysis (PCA). The dataset is trained and tested using hold out and K-fold cross-validation methods. PCA method is used to grab the essential features (7 out of 31) from the huge multivariate dataset and used for training and testing the SVM and ANN models. Model performance is measured on various metrics like accuracy, error, sensitivity, and specificity. All experiments are conducted using the R studio version 1.0.143. During the study, on the preliminary phase, many redundant features of datasets are removed with the data cleaning process and PCA is used to extract the 90% significant features in the prediction task. The finding of the paper shows that PCA played significant role in the enhancement of the prediction accuracy of the ANN and SVM. Also, the ANN classifier outperformed SVM in binary classification. We recommend this study to be used as a real-time prediction on Hospital official website in the future.
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Acknowledgements
The corresponding author thank the UCI website to provide significant datasets to pursue this research. The authors’ institutions, Bharat Group of College and Eötvös Loránd, did not require ethical committee approval to be granted for this study.
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Mehta, D., Verma, C. (2020). Prediction of Cancer Diagnosis Patients from Fine-Needle Aspirates Using Machine Learning. In: Singh Tomar, G., Chaudhari, N.S., Barbosa, J.L.V., Aghwariya, M.K. (eds) International Conference on Intelligent Computing and Smart Communication 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0633-8_33
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