Hybrid Deep Learning and Optimization Algorithm for Breast Cancer Prediction Using Data Mining


  • G. Rajasekaran Research Scholar, Department of Computer Science Engineering, SCSVMV University, Kancheepuram, Tamilnadu, India
  • P. Shanmugapriya Associate Professor, Department of Computer Science Engineering, SCSVMV University, Kancheepuram, Tamilnadu, India


Breast cancer prediction, Mining, LDA feature extraction, WHO model, hyper parameters fine-tuning, AERNN model


Breast Cancer is the uncontrollable growth of cells by abnormal activities of genes. 2 in 10 women in world will be identified with breast cancer in her lifetime. On average, every 5 minutes a woman is identified with breast cancer in the world. So, there is a huge need for intelligent early prediction methods to support a health care peoples for increasing the survival rate of the patients. Recently, data mining approach of Deep Learning (DL) and machine learning (ML) contributes beneficial role in medical field for detection and classifications of diseases. The accuracy of prediction is reduced due to the imbalanced nature of data with unequal distribution of the positive and negative classes. To overcome this issue, the breast cancer prediction is presented by using a Hybrid algorithm such as Linear Discriminant Analysis (LDA), Wild Horse Optimization (WHO) and Advanced Elman Recurrent Neural Network (AERNN) methods in this work. A LDA model is used to remove a features, WHO model is used for feature reduction and tuning a AERNN’s hyper parameters and Optimized AERNN model for classifications. The proposed method has outperformed by achieving a result of Precision (98.51%), Recall (98.65%), Accuracy (97.88%) and F1 score (98.32%) and also in the performances of error evaluation of RMSE (1.006) and MAE (1.986) than the prior methods respectively.


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Direction of Linear class separation




How to Cite

G. Rajasekaran and P. Shanmugapriya, “Hybrid Deep Learning and Optimization Algorithm for Breast Cancer Prediction Using Data Mining”, Int J Intell Syst Appl Eng, vol. 11, no. 1s, pp. 14–22, Jan. 2023.