Early-Diabetes-Detection-Project

📌 Overview

The Early Diabetes Detection Project is a machine learning classification project that aims to predict the likelihood of diabetes at an early stage using patient symptoms and demographic data. The dataset used in this project was collected from Sylhet Diabetes Hospital, Bangladesh, and consists of 520 instances with 16 features.

📊 About the Dataset

Dataset Characteristics:

• Type: Multivariate
• Subject Area: Computer Science / Healthcare
• Associated Task: Classification
• Feature Type: Categorical, Integer
• Number of Instances: 520
• Number of Features: 16
• Missing Values: Yes

This dataset comprises information on signs and symptoms of newly diagnosed diabetic patients or individuals at risk. The data was gathered via direct questionnaires and validated by medical professionals.

Reference Paper:

"Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques"
Authors: M. M. F. Islam, Rahatara Ferdousi, Sadikur Rahman, Humayra Yasmin Bushra
Published in: Computer Vision and Machine Intelligence in Medical Image Analysis (2019)

📑 Features and Variables

Feature	Type	Description
age	Integer	Age of the patient (20-65 years)
gender	Categorical	Gender (1 = Male, 2 = Female)
polyuria	Binary	Excessive urination (1 = Yes, 2 = No)
polydipsia	Binary	Excessive thirst (1 = Yes, 2 = No)
sudden_weight_loss	Binary	Sudden weight loss (1 = Yes, 2 = No)
weakness	Binary	Feeling of weakness (1 = Yes, 2 = No)
polyphagia	Binary	Excessive hunger (1 = Yes, 2 = No)
genital_thrush	Binary	Fungal infection (1 = Yes, 2 = No)
visual_blurring	Binary	Blurry vision (1 = Yes, 2 = No)
itching	Binary	Experience of itching (1 = Yes, 2 = No)
irritability	Binary	Irritability (1 = Yes, 2 = No)
delayed_healing	Binary	Slow wound healing (1 = Yes, 2 = No)
partial_paresis	Binary	Muscle weakness (1 = Yes, 2 = No)
muscle_stiffness	Binary	Muscle stiffness (1 = Yes, 2 = No)
alopecia	Binary	Hair loss (1 = Yes, 2 = No)
obesity	Binary	Obesity (1 = Yes, 2 = No)
class	Binary	Diabetes diagnosis (1 = Positive, 2 = Negative)

🚀 Project Goals

• Develop a machine learning model to predict early-stage diabetes risk.
• Perform exploratory data analysis (EDA) to understand feature importance.
• Implement data preprocessing, handling missing values, and feature engineering.
• Train and evaluate multiple classification models (Logistic Regression, Decision Tree, Random Forest, SVM, etc.).

🛠 Installation & Setup

1. Clone the repository:

   git clone https://github.com/your-username/Early-Diabetes-Detection-Project.git
   cd Early-Diabetes-Detection-Project

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Jupyter Notebook or Python script to train models:

   jupyter notebook

📌 Usage

• Load and preprocess the dataset
• Perform exploratory data analysis (EDA)
• Train and evaluate machine learning models
• Visualize model performance

📈 Model Performance

The project will compare different models based on accuracy, precision, recall, and F1-score. Results and visualizations will be included in the analysis.

🤝 Contribution

Contributions are welcome! Feel free to:

• Submit issues
• Suggest improvements
• Create pull requests

📜 License

This project is open-source and available under the MIT License.

🏷 Acknowledgments

Special thanks to the authors of the dataset and research paper for making this valuable dataset publicly available.

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