This project focuses on using machine learning algorithms to develop models for diabetes detection. Four different models were implemented and evaluated: Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), and Neural Network (NN).
Diabetes is a chronic medical condition that affects millions of people worldwide. Early detection and diagnosis of diabetes can lead to better management and treatment outcomes. This project aims to provide a solution for automating the diabetes detection process using machine learning models.
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.
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The dataset 'diabetes.csv' file is also included in this repo.
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For more info on dataset please visit Kaggle's website: Pima Indians Diabetes Dataset from Kaagle
To run the project locally, follow these steps:
- Clone the repository:
git clone https://github.com/karan-panda/diabetes-detection-project-using-MLmodels.git-
Navigate to the project directory
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Install the required dependencies:
pip install -r requirements.txt- Explore the model implementations and experiment with different datasets. The models are implemented in separate files for easy understanding and usage.
The project includes the following machine learning models for diabetes detection:
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Linear Regression: A linear model that predicts the probability of diabetes based on the input features.
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Logistic Regression: A binary classification model that estimates the probability of diabetes occurrence using logistic function.
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K-Nearest Neighbors (KNN): A non-parametric model that classifies instances based on the similarity to k nearest training samples.
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Neural Network (NN): A deep learning model with multiple layers that learns complex patterns in the dataset to predict diabetes.
Contributions to this project are welcome! If you find any issues or want to add new features or models, feel free to open an issue or submit a pull request.
Please ensure that your contributions adhere to the following guidelines:
- Follow the coding style and conventions used in the existing codebase.
- Document any new features or changes you make.
Feel free to use the code in any way you want.