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Table of Contents:

Chapter 1: Introduction
1.1 Background of the Study
1.2 Objective of the Study
1.3 Limitation of the Study
1.4 Scope of the Study

Chapter 2: Literature Review
2.1 Overview of Machine Learning
2.2 Anomaly Detection in Healthcare
2.3 Previous Studies on Anomaly Detection in Healthcare

Chapter 3: Research Methodology
3.1 Data Collection Methods
3.2 Data Preprocessing Techniques
3.3 Machine Learning Algorithms Used
3.4 Evaluation Metrics

Chapter 4: Discussion of Findings
4.1 Analysis of Anomaly Detection Results
4.2 Comparison of Different Machine Learning Algorithms
4.3 Interpretation of Results

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications of the Study
5.3 Recommendations for Future Research

Brief Overview:

Machine Learning has gained significant attention in the healthcare industry due to its ability to identify anomalies in medical data. Anomaly detection plays a crucial role in healthcare as it helps in identifying unusual patterns or outliers that may indicate potential health issues or errors in the data.

Machine Learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks are commonly used for anomaly detection in healthcare. These algorithms analyze large datasets to identify patterns and anomalies that may indicate potential health risks or errors in medical records.

Anomaly detection in healthcare can be used for various purposes, including fraud detection, disease diagnosis, and monitoring patient health. By detecting anomalies in medical data, healthcare providers can improve patient care, reduce medical errors, and enhance overall efficiency in the healthcare system.

This final year project aims to explore the use of Machine Learning for anomaly detection in healthcare. By conducting a thorough literature review, analyzing different machine learning algorithms, and discussing the findings, this project seeks to contribute to the existing body of knowledge in this area.

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