Work

Enhancing Breast Cancer Detection with Dimensionality Reduction Techniques: A Study Using PCA and LDA on Wisconsin Breast Cancer Data

Year: 2024

Type: conference-paper

Abstract: Breast cancer is the leading cause of cancer-related mortality among women, presenting a significant health challenge. This study introduces a method that leverages dimensionality reduction, specifically Principal Component Analysis (PCA), to enhance the accuracy of existing breast cancer detection techniques. By applying PCA and Linear Discriminant Analysis (LDA), our research utilizes the Wisconsin breast cancer dataset to validate the improved performance of these methods. The findings indicate that incorporating PCA into the detection models significantly enhances prediction accuracy and model performance.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2024.11.128

Authors: Gani Esen, Aizhan Altaibek, Jandos Amankulov, Bazargul Matkerim, Marat Nurtas

Institutions: International Information Technologies University, Ionosphere Institute, Kazakh Institute of Oncology and Radiology, Al-Farabi Kazakh National University

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Ministry of Education and Science of the Republic of Kazakhstan, Al-Farabi Kazakh National University