Breast Cancer Classification Using Machine Learning. An Empirical Study

Diploma Thesis , 2020 , 77 Pages , Grade: 3.55

Medicine - Diagnostics

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.

Inhaltsverzeichnis (Table of Contents)

• CHAPTER ONE
  • INTRODUCTION
  • Background of the study.
  • Statement of the Problem.
  • Aim and Objective of the study
  • Research Questions ..........\n
  • Significance of study.
  • Scope of the study.
  • Limitation of the study.
  • Operational definition of terms.
• CHAPTER TWO
  • LITERATURE REVIEW.
  • Overview of Breast Cancer
  • Risk Factors
  • Symptoms of breast cancer
  • Screening/Diagnosis of breast cancer
  • Treatment of breast cancer....
  • Breast cancer type using system gaining knowledge.
  • Diagnosis and Treatment of Breast Cancer
  • Advantages and Disadvantages of Machine learning
  • EMPIRICAL STUDIES..
• CHAPTER THREE
  • SYSTEM DESIGN AND ANALYSIS.
  • Data Collection Methods
  • DESIGN LANGUAGES, TOOLS, AND TECHNIQUES OF THE PROPOSED SYSTEM....
  • TECHNIQUES OF PROPOSED SYSTEM
  • ANALYSIS OF THE EXISTING SYSTEM.
  • ANALYSIS OF THE PROPOSED SYSTEM.
  • DESIGN OF THE PROPOSED SYSTEM.
  • FUNCTIONALITY OF THE SYSTEM.
• CHAPTER FOUR
  • !!!IMPLEMENTATION AND DOCUMENTATION
  • SYSTEM TESTING.
  • Test Plan.........
  • Test Data
  • Test Result
• CHAPTER FIVE
  • SUMMARY, CONCLUSION AND RECOMMENDATION.
  • Summary
  • Conclusion
  • Recommendations......
  • SUGGESTION FOR FUTURE STUDIES.

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This study aims to develop a machine learning-based system for classifying breast cancer. The primary goal is to provide a more efficient and accurate method for diagnosing breast cancer, potentially leading to improved treatment outcomes.

• Breast cancer classification using machine learning
• Application of machine learning algorithms to medical diagnosis
• Analysis of breast cancer data for improved diagnosis
• Evaluation of the accuracy and efficiency of the developed system
• Potential implications for breast cancer diagnosis and treatment

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter One provides an introduction to the research, outlining the background, problem statement, objectives, research questions, significance, scope, limitations, and operational definitions of terms.
• Chapter Two delves into a comprehensive literature review, covering various aspects of breast cancer, including its overview, risk factors, symptoms, screening and diagnosis methods, treatment options, and the use of machine learning in breast cancer diagnosis. It also explores empirical studies related to the topic.
• Chapter Three focuses on the system design and analysis, outlining the data collection methods, design languages, tools, and techniques employed in the proposed system. It analyzes both the existing and proposed systems, discussing the functionality of the system in detail.
• Chapter Four discusses the implementation and documentation of the system, covering system testing, test planning, test data, and test results.

Schlüsselwörter (Keywords)

The primary keywords and focus topics of this work revolve around breast cancer diagnosis, machine learning, data analysis, system design, and implementation. Key concepts include breast cancer classification, machine learning algorithms, data collection methods, system testing, and evaluation metrics for the developed system. These terms reflect the core research focus and the main themes explored throughout the study.

• Course: Computer Science
• Grade: 3.55
• Author: Akor Ugwu (Author)
• Publication Year: 2020
• Pages: 77
• Catalog Number: V1012996
• ISBN (eBook): 9783346404824
• ISBN (Book): 9783346404831
• Language: English
• Tags: breast, cancer, classification, using, machine, learning, empirical, study