| Category | Dissertation | Subject | Computer Science |
|---|---|---|---|
| University | Module Title | Data Mining and Machine Learning Dissertation |
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. The research focuses on key factors like serum creatinine and ejection fraction to predict mortality or readmission risk. The models showed a 74% prediction accuracy and a 17% improvement in early disease detection, leading to better treatment plans. Overall, students learn that how ML can enhance decision-making and resource utilization in HF management.
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