The study will classify breast cancers into foremost problems: (Benign tumor and Malignant tumor). A benign tumor is a most cancers does now not invade its surrounding tissue or spread around the host. A malignant tumor is another kind of cancers which can invade its surrounding tissue or spread around the frame of the host. Benign cancers on uncommon event can also surely result in someone’s death, but as a fashionable rule they're no longer nearly as horrific because the malignant cancers. The malignant cancers at the contrary are like those killer bees. In this situation, you do not need to be doing something to them or maybe be everywhere near their hive, they will just spread out and attack you emass – they could even kill the individual if they are extreme enough.
Manual manner of cancer category into benign and malignant may be very tedious, susceptible to human error and unnecessarily time consuming. The proposed system while constructed can robotically classify the sort of most cancers into the safe (benign) and also the risky (malignant). This machine plays this role through the usage of machine getting to know algorithm. The following is the extensive of this new system: Classification mistakes could be notably removed, early analysis of disorder, removal of possible human mistakes and the device does no longer die. However, the researcher seeks to detect and assess the class of breast using Machine learning.
Table of Contents
CHAPTER ONE
INTRODUCTION
1.1 Background of the study
1.2 Statement of the Problem
1.3 Aim and Objective of the study
1.4 Research Questions
1.5 Significance of study
1.6 Scope of the study
1.7 Limitation of the study
1.8 Operational definition of terms
CHAPTER TWO
LITERATURE REVIEW
2.1 Overview of Breast Cancer
2.2 Risk Factors
2.2.1 Risk Factors within Control
2.2.2 Risk Factors beyond Control
2.3 Symptoms of breast cancer
2.4 Screening/Diagnosis of breast cancer
2.5 Treatment of breast cancer
2.6 Breast cancer type using system gaining knowledge
2.7 Diagnosis and Treatment of Breast Cancer
2.8 Advantages and Disadvantages of Machine learning
2.9 EMPIRICAL STUDIES
CHAPTER THREE
SYSTEM DESIGN AND ANALYSIS
3.1 Data Collection Methods
3.2 DESIGN LANGUAGES, TOOLS, AND TECHNIQUES OF THE PROPOSED SYSTEM
3.2.1 TOOLS
3.3 TECHNIQUES OF PROPOSED SYSTEM
3.4 ANALYSIS OF THE EXISTING SYSTEM
3.5 ANALYSIS OF THE PROPOSED SYSTEM
3.5.1 SIGNIFICANT OF THE PROPOSED SYSTEM
3.5.2 FEATURES OF THE PROPOSED SYSTEM
3.5.3 COST AND BENEFITS OF THE PROPOSED SYSTEM
3.6 DESIGN OF THE PROPOSED SYSTEM
3.6.1 ARCHITECTURAL DIAGRAM OF THE PROPOSED SYSTEM
3.6.2 DESIGN ALGORITHM
3.6.2.1 LOGISTIC REGRESSION
3.6.2.2 RANDOM FOREST
3.6.2.2.1 Tree Methods
3.6.2.2.2 Intuition Behind the Splitting
3.6.2.3 SUPPORT VECTOR MACHINE
3.7 FUNCTIONALITY OF THE SYSTEM
CHAPTER FOUR
IMPLEMENTATION AND DOCUMENTATION
4.1 SYSTEM TESTING
4.2 Test Plan
4.3 Test Data
4.4 Test Result
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 Summary
5.2 Conclusion
5.3 Recommendations
5.4 SUGGESTION FOR FUTURE STUDIES
Research Objectives and Themes
The primary aim of this study is to develop a machine learning system capable of classifying breast cancer into benign and malignant cases to improve diagnostic accuracy and early detection while minimizing human errors associated with manual classification methods.
- Application of Machine Learning algorithms (Logistic Regression, Random Forest, SVC) for medical diagnosis.
- Exploration of the Wisconsin breast cancer dataset for model training and analysis.
- Evaluation of model performance using confusion matrices and classification reports.
- Identification of risk factors and the role of early screening in mortality reduction.
- Comparison of machine learning methodologies vs. traditional clinical classification.
Excerpt from the Book
3.6.2.3 SUPPORT VECTOR MACHINE
Support Vector Machines (SVMs) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. For this project, the classification method is discussed.
Given a set of training examples, each marked for belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier.
A SVM model is a representation of the examples of a point in space mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that space and predicted to belong to a category based on which side of the gap they fall on.
The basic intuition behind SVMs can be explained through the Figure 3.11
Summary of Chapters
CHAPTER ONE: Provides an introduction to the severity of breast cancer globally and in Nigeria, outlining the problem statement and the objectives of utilizing machine learning for classification.
CHAPTER TWO: Reviews literature on breast cancer stages, risk factors, traditional diagnosis/treatment methods, and existing empirical studies on media campaigns and automated diagnostic systems.
CHAPTER THREE: Details the system design, including data collection from the Wisconsin dataset, the choice of Python as a programming language, and the algorithmic approaches used (Logistic Regression, Random Forest, SVM).
CHAPTER FOUR: Covers the implementation and documentation of the model, presenting test results, confusion matrices, and performance metrics for the selected algorithms.
CHAPTER FIVE: Summarizes the findings, draws conclusions on the effectiveness of the proposed SVM model, and provides recommendations for future improvements in automated breast cancer classification.
Keywords
Breast Cancer, Machine Learning, Logistic Regression, Random Forest, Support Vector Machine, SVC, Diagnostic Accuracy, Wisconsin Dataset, Classification, Early Detection, Benign, Malignant, Healthcare, Pattern Recognition, Algorithmic Diagnosis.
Frequently Asked Questions
What is the primary focus of this research?
The research focuses on utilizing machine learning techniques to automate the classification of breast cancer into benign and malignant categories to assist pathologists and reduce human error.
What are the central themes of this work?
The work centers on data analysis, algorithm selection for classification, the importance of early detection, and the role of healthcare education and screening awareness.
What is the main objective of the study?
The objective is to detect and classify breast cancer using a machine learning system to provide an objective, systematic, and accurate prognostic tool for gynecologists and medical professionals.
Which scientific methods are employed?
The study utilizes supervised learning algorithms, specifically Logistic Regression, Random Forest, and Support Vector Machines (SVM), and performs exploratory data analysis (EDA) on the Wisconsin breast cancer dataset.
What does the main part of the document address?
The main part covers the literature review on cancer types and risk factors, the selection of development tools (Python, Scikit-Learn), the mathematical design of the algorithms, and the comparative evaluation of the models.
Which keywords characterize this paper?
Keywords include Breast Cancer, Machine Learning, Classification, SVM, Wisconsin Dataset, and Diagnostic Accuracy.
Why was the Support Vector Machine (SVM) chosen for the final model?
The SVM model was selected because it achieved the highest classification accuracy (98%) and showed the lowest Type 2 error rate compared to Logistic Regression and Random Forest.
What role does the confusion matrix play in this study?
The confusion matrix is used to visualize the performance of the classification models by identifying true positives, true negatives, false positives, and false negatives to pinpoint where the model misclassifies data.
- Arbeit zitieren
- Akor Ugwu (Autor:in), 2020, Breast Cancer Classification Using Machine Learning. An Empirical Study, München, GRIN Verlag, https://www.grin.com/document/1012996
