ItemConference Paper

Concernment of Feature Selection Using Classification Algorithms and Developing the Web Frame for Breast Cancer Prediction

Title
Concernment of Feature Selection Using Classification Algorithms and Developing the Web Frame for Breast Cancer Prediction
Creator
Deepa, B.G.; Ayshwarya, B.; Senthil, S.
Description
Breast cancer is invasive cancer and it is the most common cancer diagnosed in women. The survival rate of breast cancer patients is increasing due to timely detection, better empathy about the disease, and new tailored approach for the treatment. Even hormonal imbalance, environmental factors, gene mutation, and lifestyle are also the reasons for breast cancer. Stages of breast cancer majorly depend on the size of the tumor as well as the spreading of cancer to the lymph nodes. An instinctive disease detection system and computer-aided diagnosis will help the medical practitioners in early prediction of breast cancer using machine learning algorithms. In this paper, Random Forest for ranking the features by assigning the weights and selection of features using support vector machine and Nae Bayes are used. The Breast Cancer Wisconsin Dataset from the UCI Repository has been taken for examination purposes. Features selected from support vector machine and Naive Bayes have been tested by using seven different classifiers: logistic regression, random forest, K-nearest neighbor, support vector classifier, linear support vector classifier, Gaussian Naive Bayes, and decision tree. Based on the experimental results with 7030 and 8020 splits, 7030 is obtained with the best accuracy. Support vector machine with 12 features resulted in an accuracy of 97.66% and Nae Bayes with 17 features resulted in an accuracy of 96.49% with the improved results as compared to without feature selection. As support vector machine resulted with best accuracy with 12 features, by using these 12 features, web application for the prediction of breast cancer has been developed using Web framework using Python Flask, PyCharm IDE, and the instance has been executed virtually in the Amazon EC2 cloud Platform. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Smart Innovation, Systems and Technologies;Volume;396 SIST;pp.371-385
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Amazon EC2; Breast cancer; Feature selection; Machine learning; Nae Bayes; PyCharm; Python Flask; Random forest; Support vector machine
Coverage
Deepa B.G., Department of Computer Science, CHRIST University, Bangalore, India; Ayshwarya B., Computer Science, Kristu Jayanti College, Bangalore, India; Senthil S., Department of Computer Science, CHRIST University, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 21903018; ISBN: 978-981978095-2;
Format
online
Language
English
Type
Conference paper

Citation

Deepa, B.G.; Ayshwarya, B.; Senthil, S., “Concernment of Feature Selection Using Classification Algorithms and Developing the Web Frame for Breast Cancer Prediction,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25665.

Output Formats