Student Work (Dissertation) | COMP9005 Machine Learning for Disease Detection | University of Kent

Student | Client Work (Dissertation) | COMP9005 Machine Learning in Health Informatics | MSc AI Output Video | University of Kent

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🚀 Video Details:
📘 COMP9005 MSc Dissertation | University of Kent (Canterbury, UK)
This video presents a full dissertation titled "Machine Learning Applied to Medical Data" under the MSc Advanced Computer Science program at the University of Kent. The research explores machine learning applications in the prediction, classification, and diagnosis of medical conditions using real or synthetic datasets.

🧠 Key Topics Covered:
– Data preprocessing in healthcare datasets
– Feature engineering for clinical relevance
– Model selection: Random Forest, SVM, Logistic Regression
– Performance evaluation (Accuracy, AUC, Precision-Recall)
– Ethical and interpretability considerations in medical AI

📂 Dataset: Real-world / Simulated healthcare records
🛠️ Tools: Python, Scikit-learn, Pandas, Matplotlib
🎓 Program: MSc Advanced Computer Science
📍 Location: Canterbury, England
🏫 University: University of Kent
📘 Module: COMP9005 – Dissertation

This work contributes to the growing field of AI for medical diagnosis, offering valuable insights into how machine learning can enhance predictive healthcare systems.

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