https://github.com/vickshan001/diabetes-prediction-using-machine-learning-ci512-project-

Machine learning project (2021) predicting diabetes using the Pima Indians dataset. Compared KNN, Decision Tree, MLP, and more for accuracy.
https://github.com/vickshan001/diabetes-prediction-using-machine-learning-ci512-project-

classification diabetes-prediction machine-learning mlp pima-indians-dataset python scikit-learn

Last synced: 6 months ago
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Machine learning project (2021) predicting diabetes using the Pima Indians dataset. Compared KNN, Decision Tree, MLP, and more for accuracy.

• Host: GitHub
• URL: https://github.com/vickshan001/diabetes-prediction-using-machine-learning-ci512-project-
• Owner: vickshan001
• Created: 2025-03-29T18:50:38.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2025-03-29T18:51:50.000Z (6 months ago)
• Last Synced: 2025-03-29T19:32:11.956Z (6 months ago)
• Topics: classification, diabetes-prediction, machine-learning, mlp, pima-indians-dataset, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🧠 Diabetes Prediction Using Machine Learning (CI512 Project)

A machine learning project using the **Pima Indians Diabetes Dataset** to predict whether a patient has diabetes. Built in **2021** for the CI512 – Intelligent Systems module, this project applies and compares multiple classification algorithms to identify the most accurate model.

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## 📊 Dataset

- **Source**: National Institute of Diabetes and Digestive and Kidney Diseases  

- **Population**: Female patients over 21 years old of Pima Indian heritage  

- **Features**:

  - Pregnancies  

  - Plasma glucose concentration  

  - Diastolic blood pressure  

  - Triceps skin fold thickness  

  - Insulin  

  - BMI  

  - Diabetes Pedigree Function  

  - Age  

  - Outcome (0 or 1)  

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## 🤖 Algorithms Used

1. **K-Nearest Neighbors (KNN)**  

   - Predicts based on the distance to nearest data points.  

   - Chosen for its high accuracy with classification tasks.

2. **Decision Tree Classifier**  

   - Easy-to-interpret model using rule-based branching.  

   - Good for explaining predictions.

3. **Random Forest Classifier**  

   - Ensemble method combining multiple decision trees.  

   - Initially included, but later excluded due to inconsistent results.

4. **Multilayer Perceptron (MLP)**  

   - Neural network model with hidden layers.  

   - Provided the best prediction accuracy on this dataset.

5. **Stacking Classifier**  

   - Combines multiple models to improve prediction using meta-learning.

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## 🧪 Evaluation Method

- **Data Cleaning**: Replaced 0 values with feature-wise mean (to handle missing values).  

- **Validation**: 10-fold cross-validation  

- **Split**: 70% Training, 30% Testing  

- **Visualization**: Line graphs for model accuracy comparison

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## 📈 Results

- **Random Forest** was excluded due to poor alignment with other models.  

- **MLP (Multilayer Perceptron)** outperformed all other classifiers.  

- **Stacking** showed promise by combining model strengths.

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## 🛠 Technologies

- Python  

- Pandas  

- Scikit-learn  

- Matplotlib  

- NumPy

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## 👨‍💻 Developed By

**Vickshan Vicknakumaran**  

University of Brighton  

CI512 – Intelligent Systems (2021)

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