There is no wealth like Knowledge
                            No Poverty like Ignorance
ARPN Journals

ARPN Journal of Engineering and Applied Sciences >> Call for Papers

ARPN Journal of Engineering and Applied Sciences

A comprehensive machine learning technique for Ovarian Cancer prediction

Full Text Pdf Pdf
Author V. Mnssvkr Gupta, K. Vssr Murthy, R. Shiva Shankar and J. Varshini
e-ISSN 1819-6608
On Pages 530-537
Volume No. 18
Issue No. 05
Issue Date April 05, 2023
Keywords ovarian cancer (OC), machine learning (ML), convolutional neural network (CNN), multi-layer perceptron (MLP), random forest (RF), ensemble algorithms (EA).


On the list of most prevalent cancers in women, ovarian cancer comes in eighth place. Because of standard screening and surveillance limitations, it is clinically impractical to analyse tumour molecular markers to predict therapy response. A relevant dataset with the necessary features was chosen for the prediction task of whether the patient has ovarian cancer or not. This approach uses Multilayer perceptron, ELM with AdaBoost, XGboost, LSTM, and a new CNN with Random Forest algorithms to predict Ovarian Cancer. The assessment parameters for each model were calculated for accuracy, precision, recall, F1-Score, Jaccard Index, and Error rate. The algorithms are then compared based on these metrics to determine the best algorithm. The CNN with Random Forest was the best method, with 95 to 100% accuracy. The CNN with Random Forest algorithm outperformed the individual existing ensemble techniques studied.


GoogleCustom Search

Seperator Publishing Policy Review Process Code of Ethics

© 2023 ARPN Publishers