Breast Cancer Diagnostic Using Machine Learning - Applying Supervised Learning Techniques to Coimbra and Wisconsin Datasets - Master’s Thesis 2023

Aug 3, 2026

Upload by Noah

Breast cancer represents a major global health challenge, reported with about 2.2 million new cases and 700,000 deaths in 2020. Conventional diagnosis relies heavily on expert judgement and can show notable variation in accuracy. Supervised machine learning models are applied to improve early detection and diagnostic precision, evaluating Decision Trees, K-Nearest Neighbors, Support Vector Machines, and Logistic Regression on the Coimbra and Wisconsin datasets. Model comparison includes hyperparameter and distance-measure optimization, with feature selection and Principal Component Analysis to identify effective configurations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages72

Reading Time3 h 1 min

AccessFree to download

Abstract

Acknowledgments

Symbols and abbreviations

1. Introduction

1.1. Background and Motivation

1.2. Aim and research question

1.2.1. Significance of study

2. Theoretical Background

2.1. Breast cancer

2.2. Machine learning

2.3. Algorithms

2.4. Assessing Machine Learning Models: The Key Role of Precision, Recall, F1-score, and Accuracy

BREAST CANCER DIAGNOSTIC USING MACHINE LEARNING Applying Supervised Learning Techniques to Coimbra and Wisconsin Datasets Lappeenranta–Lahti University of Technology LUTMaster’s Programme in Computational Engineering 2023 Vikas Kushwaha Examiners: Associate Professor Lassi Roininen Post-Doctoral Researcher Jyrki Savolainen ABSTRACT Lappeenranta–Lahti University of Technology LUTLUT School of Engineering Science Computational Engineering Vikas Sudarshan Kushwaha BREAST CANCER DIAGNOSTIC USING MACHINE LEARNING Applying Supervised Learning Techniques to Coimbra and Wisconsin Datasets Master’s thesis 2023 71 pages, 13 figures, 12 tables Examiners:Associate Professor Lassi Roininen and Jyrki Savolainen, Post-Doctoral Researcher Keywords: Breast cancer diagnosis, Machine learning, Cancer detection, Predictive Modelling Breast cancer poses a significant global health concern, with approximately 2.2 million new cases and 700,000 deaths reported in 2020. Traditional diagnostic approaches which predominantly depend on expert judgement, have been associated with substantial variability in accuracy. To bridge this gap ML models are used to improve diagnostic out of which the present research investigates the potential of specific machine learning algorithms—Decision Trees, K-Nearest Neighbors, Support Vector Machines, and Logistic Regression—with an overarching objective of improving early detection and enhancing the precision of breast cancer diagnosis. The study utilizes the Breast Cancer Coimbra Dataset and the Wisconsin Diagnostic Breast Cancer Dataset for model training and evaluation. A comprehensive comparative analysis of these models is conducted, with a focus on optimizing hyperparameters and distance measures to ascertain the most effective configurations. Further, the influence of feature selection methods and Principal Component Analysis on model performance is explored. Logistic Regression and Support Vector Machines models demonstrated remarkable performance, surpassing the predictive accuracy of models reported in current literature, with accuracies reaching up to 99.42% . This research could serve as a foundation for future studies applying machine learning models in breast cancer diagnostics, emphasizing the potential of machine learning as a robust tool in medical diagnostics. ACKNOWLEDGEMENTS I want to express my deepest gratitude to my mom, dad, and brothers who have provided unwavering support throughout my master's journey. Their love and reassurance during times of doubt have provided the strength I needed to persevere. I can confidently say that this achievement would not have been possible without them. I am immensely thankful for their enduring faith in my abilities and for their ceaseless support. I would also like to thank my supervisors, Jyrki Savolainen and Lassi Roininen, for their invaluable feedback, which has greatly enhanced the quality and clarity of this work. ABBREVIATIONS BC Breast Cancer ML Machine Learning BCCD Breast Cancer Coimbra Dataset WDBC Wisconsin Diagnostic Breast Cancer KNN K-Nearest Neighbors SVM Support Vector Machines LR Logistic Regression DT Decision Tree DCIS Ductal Carcinoma in Situ ASIR Age-Standardized Incidence Rate HDI Human Development Index DALY Disability-Adjusted Life Years HER2 Human Epidermal growth factor Receptor 2 BRCA1 Breast Cancer gene 1 BRCA2 Breast Cancer gene 2 HRT Hormone Replacement Therapy MIR Mortality-to-Incidence Ratio PCA Principal Component Analysis TP True Positive TN True Negative FP False Positive FN False Negative Table of contents Abstract Acknowledgments Symbols and abbreviations 1. Introduction ................................................................................................................... 8 1.1. Background and Motivation................................................................................... 9 1.2. Aim and research questio

FAQs

Which supervised learning algorithms are investigated for breast cancer diagnosis?

The study evaluates Decision Trees, K-Nearest Neighbors, Support Vector Machines, and Logistic Regression.

What datasets are used for training and evaluation?

Training and evaluation use the Breast Cancer Coimbra Dataset and the Wisconsin Diagnostic Breast Cancer Dataset.

How is model performance improved and compared in the research?

Performance is compared through hyperparameter and distance-measure optimization, and by analyzing the impact of feature selection methods and Principal Component Analysis (PCA).

What results do Logistic Regression and Support Vector Machines achieve?

Logistic Regression and Support Vector Machines show strong predictive performance, reaching accuracies up to 99.42% and surpassing reported accuracies in current literature.

Aug 3, 2026

Upload by Noah