Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (6): 417-425.

### Relative Examination of Breast Malignant Growth Analysis Utilizing Different Machine Learning Algorithms

Rajan Prasad Tripathia,*, Sunil Kumar Khatria, and Darelle Van Greunenb

1. aDepartment of IT and Engineering, Amity University in Tashkent, Tashkent, Uzbekistan;
bCenter for Community Technologies, Nelson Mandela University, Port Elizabeth, South Africa
• Submitted on  ;  Revised on  ; Accepted on
• Contact: * E-mail address: rajantripathi22@gmail.com
• About author:Rajan Prasad Tripathi is a PhD Scholar in Amity University Uttar Pradesh, Noida, India. He is also serving as assistant Professor in Dept. of IT and Engineering in Amity University in Tashkent, Uzbekistan. His research interest includes Data Analysis, Machine learning in health care.Dr. Sunil Kumar Khatri is serving as Director of campus, Amity University in Tashkent, Uzbekistan. His research interests include Data Analysis, Machine Learning and Artificial Intelligence.Dr. Darelle Van Greunen is serving as Director Center for Community Technologies, Nelson Mandela University, Port Elizabeth, South Africa. Her research interest includes Data Analysis, Machine Learning and Artificial Intelligence and application of AI in health domain.

Abstract: Tumour malignancy has caused a very high number of women's deaths. Proper hyperparametric ML technique can help to efficiently detect tumours. In this work, various algorithms to analyse the Wisconsin breast cancer data set have been studied. Deep learning with Adam Gradient Descent Learning has been used, which combines the advance features of adaptive gradient and rms propagation. Linear regression, Shallow SoftMax regression, and Deep neural network SoftMax regression are used for the classification of benign and malignant tumour and have calculated the AUC, ROC and accuracy. A unique hyperparametric change is shown in each model to work on the exactness of the model and also to chalk out correlation between different models. It is concluded in the analysis that for the WBC data set the deep neural network SoftMax regression significantly improves the accuracy from other models, and we reached a test accuracy 0f 0.956 and testing accuracy of 1.0.

Key words: breast cancer, deep learning, regression