| Category | Dissertation | Subject | Computer Science |
|---|---|---|---|
| University | Module Title | Data Mining and Machine Learning |
| Programme | P0080 MSc Dissertation |
| Word Count: | 10,000–15,000 words |
This study explores the use of machine learning (ML) algorithms to improve the identification, prognosis, and monitoring of heart failure (HF) using clinical data. It applies three Machine Learning models: Logistic Regression for hypothesis testing, Random Forest for classification, and K-means clustering for patient segmentation.
Abstract
Chapter 1: Introduction
Research Background
Problem Statement
Aims of the Project
Basic Objectives
Advanced Objectives
Research Questions
Significance of the Study
Chapter 2: Literature Review
Chapter 3: Methodology
Introduction
Dataset Description
Data Preprocessing
Implementation Details
Ethical Considerations
Conclusion
Chapter 4: Results
Introduction
Chapter 5: Discussion
Chapter 6: Conclusion and Recommendation
Conclusion
Recommendations
References
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