Voting Classification Method for the Microarray Cancer Detection

  • Megha Soni, Dr. Harish Kumar Taluja, Anuradha

Abstract

Bioinformatics is a science that is useful to store, fetch, arrange, interpret and employ the information attained from biological series and molecules. The detection of cancer in early stage has become a crucial problem in recent times. With the rapid growth, the risk of death caused by cancer is rising exponentially. Prediction can be termed as a statement related to the future event in accordance with the present situation. Nowadays, Breast cancer is emerging as the major cause of death in women all around the world. An accurate and interpretable method is needed for diagnosing women with the breast cancer for an effective treatment. Due to rapid growth in medical research, many of ensemble methods have been widely applied to diagnose breast cancer, such as Random forest. However, the drawback of this method is that they are unable to explain the reasons behind the diagnosis hence they are called black-box method. The major intend of this work is to predict the microarray cancer using machine learning (ML) algorithms. The parameters to measure the performance of the algorithm are accuracy, precision, negative predictive value, sensitivity, FNR (false-negative rate), FPR (false-positive rate). Different phases are comprised in the prediction of microarray cancer. This research makes the implementation of voting-based classification algorithm. The suggested algorithm assists in optimizing the performance up to 2% while predicting the microarray cancer.

Published
2021-06-16
How to Cite
Megha Soni, Dr. Harish Kumar Taluja, Anuradha. (2021). Voting Classification Method for the Microarray Cancer Detection. Design Engineering, 962 - 978. Retrieved from http://thedesignengineering.com/index.php/DE/article/view/2074
Section
Articles