An Expert System for Breast Cancer Prediction (ESBCP) using Decision Tree

Akhil Kumar Das; Saroj Kumar Biswas; Ardhendu Mandal

doi:10.17485/IJST/v15i45.756

Article

An Expert System for Breast Cancer Prediction (ESBCP) using Decision Tree

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i45.756

Year: 2022, Volume: 15, Issue: 45, Pages: 2441-2450

Original Article

An Expert System for Breast Cancer Prediction (ESBCP) using Decision Tree

Akhil Kumar Das^1*, Saroj Kumar Biswas², Ardhendu Mandal³

¹Department of Computer Science, Gour Mahavidyalaya, Malda, West Bengal, 732142, Mangalbari, India
²Department of Computer Science and Engineering, Assistant professor Stage I, NIT Silchar, Assam 788010, India
³Department of Computer Science and Application, University of North Bengal, West Bengal, Darjeeling734013, India

*Corresponding Author
Email: [email protected]

Received Date:05 April 2022, Accepted Date:17 October 2022, Published Date:02 December 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: Breast cancer is one of the major concerns in present day scenario. Detecting breast cancer at early stage increases the chances of survival. The objective of this research is to propose suitable feature selection method to improve the efficiency of breast cancer prediction at early stages to increase the survival rate. Methods: In this work, an expert intelligent technique has been proposed named “Expert System for Breast Cancer Prediction (ESBCP)” to detect breast cancer. To validate the results, the proposed system determines accuracy, precision, F-measure, and recall. The proposed model introduced a feature selection technique named - Undiluted Feature Set (UFS) to select the most relevant and promising features. The experimental work was carried out using Python 2.8 version in a Windows environment, taking a dataset on breast cancer from the UCI machine learning repository. There were 699 occurrences in the dataset with nine attributes and two classes. The proposed work utilized a decision tree and a new feature selection technique based on a heuristic search and the Stochastic Hill method. The experimental results were evaluated using the 10-fold Cross-Validation (CV). Findings: The experimental findings showed that the suggested model - ESBCP can accurately detect breast cancer at an early stage. As per the result, with simple decision tree the accuracy recorded 93.42 percent whereas ESBCP obtained 94.01 percent. It may seem that the improvement of 0.59 percent is very small, but for a large population even this mere change can have a greater impact. Novelty: The suggested model ESBCP and the feature selection technique - UFS have a lot of potential in the fields of medical research and bioinformatics in terms of classification capability and predictive power. Keywords: Expert System; Decision Tree; Undiluted Feature Set; Breast Cancer; Feature Selection

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Copyright

© 2022 Das et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)