LabVIEW based robust cascade predictive model for evaluating cancer prognosis

Abstract

Cancer is a disease that is found in many forms. Early diagnosis process significantly affects follow-up of the disease. As in other diseases, it is important to classify the data in cancer cases to determine whether the person belongs to healthy-patient or high-low risk groups. For this purpose, machine learning based on artificial intelligence can be used as a very effective method to follow both the progress and the treatment response process of such diseases and to reveal important features of data sets. In this publication, breast cancer diagnosis was carried out using Principal Component Analysis-Support Vector Machine (PCA-SVM) and proposed parallel Principal Component Analysis-Linear Discriminant Analysis-Support Vector Machine (PCA-LDA-SVM) model classifier algorithms, by LabVIEW. LabVIEW, known as Virtual Instrument (VI), is a graphical programming language. The durableness of the used algorithms is analyzed using accuracy, sensitivity, specificity, rand index, False Positive Rate (FPR), False Discovery Rate (FDR), False Negative Rate (FNR), Negative Predictive Value (NPV), Matthews Correlation Coefficient (MCC) parameters and status detection. The obtained results are compared with each other. After training, of the 140 data used in the test set, 130 were used for the test performance analysis and 10 data were used for the status determination of the newly entered data. Performance analysis has been examined for Polynomial and Gaussian kernel functions. The proposed parallel model provides improvement especially for the Polynomial kernel function. With the proposed model, an increase in classification accuracy was observed in the test phase compared to PCA-SVM, and it was observed that 10 data used for status determination were correctly classified. (C) 2020 Elsevier B.V. All rights reserved.