Neural Network Ensemble for the Prediction of Pathological Complete Response After Neoadjuvant Chemotherapy for Breast Cancer
Raghvi Bhardwaj1, Nishtha Hooda2

1Raghvi Bhardwaj, M.E. student in Computer Science Engineering Department, Chandigarh University, Mohali, India.
2Nishtha Hooda, Assistant Professor at the Department of Computer Science Engineering, Chandigarh University, Mohali, India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 225-230 | Volume-8 Issue-8, June 2019 | Retrieval Number: G5802058719/19©BEIESP
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Abstract: Neoadjuvant Chemotherapy is given intravenously during the treatment of breast cancer. Before the surgery, doctors suggest chemotherapy to deflate the large size of encroaching tumor. This research work propounds Neural Network Ensemble Machine Learning framework, which accomplishes ensemble of Machine Learning algorithms for constructing an systematized solution for anticipating the pathological complete response of the patients after Neoadjuvant Chemotherapy. Performance score is calculated by considering ten evaluation metrics namely, Accuracy, Mean Absolute Error, Root Mean Square Error, TP Rate, FP Rate, Precision, Recall, F-Measure, MCC, and, ROC. The outcomes are verified using K Fold cross validation technique and achieving an accuracy of 97.20%. When the execution of the proposed framework is accumulated with the accomplishment of state-of-the-art classifiers such as Bayes Net, Naïve Bayes, Logistic, Multilayer Perceptron, SMO, Voted Perceptron, etc, the results are quite promising. Machine learning can play a key role in saving lives, in the field of cancer detection.
Keyword: Machine learning, Neural network, Prediction, neoadjuvant chemotherapy.
Scope of the Article: Distributed Sensor Networks