Feature/brain stroke assessment

Brain Stroke Risk Assessment/Dataset/readme.md

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+# Dataset Source
+The data utilized for this project can be downloaded from Kaggle:
+https://www.kaggle.com/datasets/jillanisofttech/brain-stroke-dataset

Brain Stroke Risk Assessment/Model/placeholder.txt

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+

Brain Stroke Risk Assessment/README.md

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+# Brain Stroke Risk Assessment using Multi-Architecture ANNs
+
+## 📌 Project Overview
+This project targets the prediction of brain stroke clinical susceptibility using key physiological factors (e.g., age, average glucose levels, BMI, hypertension, and heart disease status). The underlying dataset presents a severe real-world class imbalance (~5% positive stroke instances), making standard predictive metrics highly misleading.
+
+To address this, we designed, executed, and benchmarked **4 distinct Artificial Neural Network (ANN) topologies** using TensorFlow/Keras to analyze architectural behaviors under high-skew target environments.
+
+---
+
+## 📊 Dataset Overview & Preview
+The raw dataset undergoes comprehensive structural preprocessing: continuous values are normalized via a `StandardScaler` pipeline, while categorical elements are mapped using a `OneHotEncoder` structure to prevent model data leakage. This safely expands our features into 19 structural input dimensions.
+
+![Dataset Table Preview](Images/dataset_preview.png)
+
+---
+
+## ⚙️ Model Architectures
+
+We built and evaluated four target configurations:
+1. **Vanilla Tabular MLP:** Our standard feedforward sequence baseline.
+2. **Regularized MLP:** Structural setup utilizing integrated `BatchNormalization` and `Dropout(0.3)` layers to explicitly penalize overfitting indicators.
+3. **Class-Weighted MLP:** Imbalance-optimized network tracking sample penalty metrics via dynamically scaled loss functions.
+4. **Residual Tabular MLP:** Advanced configuration using the Keras Functional API to map residual shortcut skip connections.
+
+---
+
+## 📈 Final Evaluation Matrix Results
+
+The performance of the respective networks evaluated against hidden test partition subsets is documented below:
+
+![Model Performance Output Matrix](Images/evaluation_matrix.png)
+
+| Model Architecture | F1-Score | PR-AUC | Primary Structural Trait |
+| :--- | :---: | :---: | :--- |
+| **1. Vanilla Tabular MLP** | 0.0377 | 0.1938 | Standard Sequential Baseline Feedforward Layers |
+| **2. Regularized MLP** | 0.0392 | 0.1729 | Integrated Batch Normalization & Dropout (0.3) |
+| **3. Class-Weighted MLP** | **0.2182** | 0.1760 | Loss function gradients scaled by class penalties |
+| **4. Residual Tabular MLP** | 0.1127 | 0.1603 | Keras Functional API Skip-Connections (Residual Blocks) |
+
+### 🔍 Key Insights & Contributions
+* **Class-Weighting Superiority:** Assigning structural sample weights based on label scarcity inside **Model 3** delivered a **~478% increase in F1-Score performance**, proving the absolute necessity of loss-scaling in medical diagnostic forecasting.
+* **Residual Signal Flow:** Incorporating functional residual skip blocks inside **Model 4** boosted F1 performance over the vanilla baseline without explicit weight parameters, confirming that shortcut routes enhance structural representation capacity for continuous features.
+
+---
+
+## 🛠️ Installation & Setup Instructions
+
+### Prerequisites
+Ensure all standard processing dependencies are installed locally:
+```bash
+pip install -r requirements.txt

Brain Stroke Risk Assessment/requirements.txt

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+pandas
+numpy
+scikit-learn
+tensorflow
+matplotlib
+seaborn