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            <h1><b>MSc Project Proposal: Heart Failure Prediction Using Machine Learning</b></h1>
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            <h2>Project Summary</h2>
            <h3>This project aims to develop a machine learning-based framework for predicting heart failure outcomes. By analysing patient health records, this study will use various machine learning models to determine the key factors contributing to heart failure and improve predictive accuracy.</h3>

            <h2>Objectives</h2>
            <h3>
              1️. Preprocess and Explore the Dataset<br>
              - Handle missing values and scale features appropriately.<br>
              - Perform exploratory data analysis (EDA) to identify correlations.<br><br>
              2️. Train and Evaluate Machine Learning Models<br>
              - Implement multiple models (Logistic Regression, Random Forest, SVM, XGBoost, ANN).<br>
              - Optimize models using hyperparameter tuning and cross-validation.<br><br>
              3️. Feature Importance and Explainability<br>
              - Use SHAP values and permutation importance to interpret key risk factors.<br>
              - Compare the impact of different features on prediction accuracy.<br><br>
              4️. Model Performance Comparison<br>
              - Evaluate models using accuracy, F1-score, and ROC-AUC metrics.<br>
              - Visualize results using confusion matrices and classification reports.<br><br>
              5️. Develop an Interactive Prediction Tool<br>
              - Build a user-friendly interface to input patient data and receive predictions.<br>
              - Ensure explainability and reliability for clinical applications.<br>
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            <h2>Expected Outcomes</h2>
            <h3>
              ✅ A machine learning framework for predicting heart failure outcomes.<br>
              ✅ Identification of key clinical indicators affecting heart failure risk.<br>
              ✅ A comparison of various machine learning techniques.<br>
              ✅ A prototype tool for real-time heart failure risk prediction.<br>
            </h3>

            <h2>Skills & Tools Required</h2>
            <h3>
              🛠 Essential: Python, Scikit-Learn, TensorFlow/PyTorch<br>
              📊 Essential: Data preprocessing, feature selection, model evaluation<br>
              📈 Preferable: SHAP analysis, statistical modeling, medical data interpretation<br>
            </h3>

            <h2>Download Dataset</h2>
            <h3>
              📥 The dataset for this project can be downloaded from <a href="https://drive.google.com/file/d/1x7t9tZ-OtPCpknnnq9JB-mpHwTrvpsrs/view?usp=sharing" target="_blank">this link</a>.
            </h3>

            <h3>For more information, please contact <a href="https://www.uwl.ac.uk/staff/massoud-zolgharni" target="_blank" rel="noopener noreferrer">Professor Massoud Zolgharni</a> or <a href="https://www.uwl.ac.uk/staff/nasim-dadashi-serej" target="_blank" rel="noopener noreferrer">Dr Nasim Dadashi</a></h3>
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