https://github.com/aneeshmurali-n/ann-diabetes-prediction

Predicting diabetes progression using an Artificial Neural Network (ANN). This project leverages the scikit-learn diabetes dataset for training and evaluation. Includes data preprocessing, model building, and performance visualization.
https://github.com/aneeshmurali-n/ann-diabetes-prediction

ann data-preprocessing data-visualization deep-learning diabetes-prediction exploratory-data-analysis keras machine-learning matplotlib neural-network numpy pandas regression scikit-learn seaborn tensorflow visualization

Last synced: about 1 month ago
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Predicting diabetes progression using an Artificial Neural Network (ANN). This project leverages the scikit-learn diabetes dataset for training and evaluation. Includes data preprocessing, model building, and performance visualization.

• Host: GitHub
• URL: https://github.com/aneeshmurali-n/ann-diabetes-prediction
• Owner: aneeshmurali-n
• License: mit
• Created: 2024-10-24T12:50:18.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-24T16:48:16.000Z (3 months ago)
• Last Synced: 2024-10-25T14:29:13.184Z (3 months ago)
• Topics: ann, data-preprocessing, data-visualization, deep-learning, diabetes-prediction, exploratory-data-analysis, keras, machine-learning, matplotlib, neural-network, numpy, pandas, regression, scikit-learn, seaborn, tensorflow, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 4.35 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Diabetes Progression Prediction using Artificial Neural Networks

Predicting diabetes progression using an Artificial Neural Network (ANN). This project leverages the scikit-learn diabetes dataset for training and evaluation. Includes data preprocessing, model building, and performance visualization.

## Project Description

This project focuses on predicting diabetes progression using an Artificial Neural Network (ANN). It leverages the diabetes dataset from scikit-learn for training and evaluation. The project demonstrates the application of ANNs in healthcare for diabetes prediction.

## Dataset

The project utilizes the diabetes dataset from scikit-learn, which contains data on diabetes progression in patients.

## Methodology

1. **Data Preprocessing:** The dataset is loaded and preprocessed, including handling missing values, outliers, and normalizing the features.

2. **Model Building:** An ANN model is designed and built using Keras.

3. **Training and Evaluation:** The model is trained on a portion of the dataset and evaluated on a separate test set. Metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) are used to assess the model's performance.

4. **Visualization:** The results are visualized using plots and charts to provide insights into the model's predictions and performance.

## Requirements

* Python 3.x

* Libraries: pandas, NumPy, scikit-learn, Keras, TensorFlow, Matplotlib, Seaborn

## Usage

1. Clone the repository

2. Install the required libraries

3. Run the Jupyter Notebook: `DL_ANN_Diabetes_Progression_Prediction.ipynb`

4. **Or, run the notebook in Google Colab:** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/aneeshmurali-n/ANN-Diabetes-Prediction/blob/main/DL_ANN_Diabetes_Progression_Prediction.ipynb)

## Results

The ANN model achieved promising results in predicting diabetes progression, with a low MAE, MSE, and a high R2 score. 

Further improvements can be explored by tuning hyperparameters, experimenting with different architectures, and incorporating more data.

## License

This project is licensed under the MIT License.