Earticle

초록

영어

The rising prevalence of breast cancer across the globe requires the application of advanced diagnostic techniques for early detection and treatment. This research uses the Wisconsin Breast Cancer Dataset to explore the efficacy of various machine-learning algorithms and ensemble techniques in predicting breast cancer. The study encompasses three significant steps: data retrieval from Kaggle, data preparation through exploratory data analysis, and predictive model formulation and evaluation. Various machine learning algorithms, including Support Vector Machine (SVM) and Random Forest (RF) were employed alongside ensemble techniques like Bagging, Boosting, and a Voting Classifier that integrates multiple models. Feature selection emerged as a pivotal task, enhancing model performance by focusing on significant attributes, thus addressing challenges like high dimensionality and overfitting while promoting model interpretability. The Voting Classifier exhibited the highest accuracy of 98.25%, with varying performance across different feature sets. The insights garnered from feature selection and machine learning models demonstrate promising capabilities for early breast cancer diagnosis, emphasizing the critical role of machine learning in advancing medical data analytics for better healthcare outcomes. This research not only underscores the potential of machine learning in medical diagnostics but also provides a comprehensive exploration of feature selection and ensemble learning in achieving superior predictive accuracy in breast cancer detection.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
III. PROPOSED METHOD
A. Step 1: Data Set and Data Preparation
B. Step 2: Data Preprocessing
C. Step 3: Modeling and Evaluation
IV. RESULT AND DISCUSSION
REFERENCES

키워드

저자

• Naruapon Suwanwijit [ Faculty of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ] Corresponding Author
• Khachon Mongkonchoo [ Faculty of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ]
• Ekasak Chitcharoen [ Faculty of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ]
• Maleerat Maliyaem [ Faculty of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ] c

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004