Machine learning for the classification of breast cancer tumor: a comparative analysis

Ranjan K. Mohapatra; Madhumita Pal; Smita Parija; Ganapati Panda; Kuldeep Dhama

doi:10.18006/2022.10(2).440.450

Authors

Ranjan K. Mohapatra Department of Chemistry, Government College of Engineering, Keonjhar, Odisha 758002, India https://orcid.org/0000-0001-7623-3343
Madhumita Pal Electronics and communication Engineering, C. V. Raman Global University, Bidyanagar, Mahura, Janla, Bhubaneswar, Odisha 752054, India
Smita Parija Electronics and communication Engineering, C. V. Raman Global University, Bidyanagar, Mahura, Janla, Bhubaneswar, Odisha 752054, India
Ganapati Panda Electronics and communication Engineering, C. V. Raman Global University, Bidyanagar, Mahura, Janla, Bhubaneswar, Odisha 752054, India
Kuldeep Dhama Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly-243122, Uttar Pradesh, India https://orcid.org/0000-0001-7469-4752

DOI:

https://doi.org/10.18006/2022.10(2).440.450

Keywords:

Breast cancer, Multilayer perceptron, K-NN, MLP, Random forest

Abstract

The detection and diagnosis of Breast cancer at an early stage is a challenging task. With the increase in emerging technologies such as data mining tools, along with machine learning algorithms, new prospects in the medical field for automatic diagnosis have been developed, with which the prediction of a disease at an early stage is possible. Early detection of the disease may increase the survival rate of patients. The main purpose of the study was to predict breast cancer disease as benign or malignant by using supervised machine learning algorithms such as the K-nearest neighbor (K-NN), multilayer perceptron (MLP), and random forest (RF) and to compare their performance in terms of the accuracy, precision, F1 score, support, and AUC. The experimental results demonstrated that the MLP achieved a high prediction accuracy of 99.4%, followed by random forest (96.4%) and K-NN (76.3%). The diagnosis rates of the MLP, random forest and K-NN were 99.9%, 99.6%, and 73%, respectively. The study provides a clear idea of the accomplishments of classification algorithms in terms of their prediction ability, which can aid healthcare professionals in diagnosing chronic breast cancer efficiently.

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