https://github.com/ysayaovong/building-a-neural-network-model-to-identify-diabetic-patients

Building a MLP (Multi-layer Perceptron) network to identify diabetic patients given a dataset that contains health information of a group of patients and whether they are diabetic or not.
https://github.com/ysayaovong/building-a-neural-network-model-to-identify-diabetic-patients

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Building a MLP (Multi-layer Perceptron) network to identify diabetic patients given a dataset that contains health information of a group of patients and whether they are diabetic or not.

• Host: GitHub
• URL: https://github.com/ysayaovong/building-a-neural-network-model-to-identify-diabetic-patients
• Owner: YSayaovong
• Created: 2024-11-24T02:30:24.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-11-24T02:32:00.000Z (about 1 month ago)
• Last Synced: 2024-11-24T03:23:23.439Z (about 1 month ago)
• Language: Jupyter Notebook
• 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 MLP Classifier

This repository contains a project aimed at predicting whether a patient has diabetes based on their health attributes using a Multi-Layer Perceptron (MLP) Classifier. 

The dataset used includes information about patients' pregnancies, glucose levels, blood pressure, BMI, and other health-related features. The neural network model was designed, trained, and evaluated to classify patients as diabetic or non-diabetic.

---

## Dataset Details

The dataset contains 800 rows, where each row represents a patient described by the following attributes:

- **Number of Pregnancies**

- **Glucose Level**

- **Blood Pressure Level**

- **Skin Thickness**

- **BMI**

- **Age**

- **Diabetic Pedigree**

- **Insulin Level**

The target attribute is **Outcome**:

- `1` = Patient has diabetes.

- `0` = Patient does not have diabetes.

You can download the dataset [here](data/diabetes.csv).

---

## Methodology

### Steps Performed:

1. **Dataset Preprocessing**:

   - Normalized the feature columns for consistent scaling.

   - Split the dataset into training (75%) and testing (25%) sets.

2. **MLP Classifier Design**:

   - Built a neural network with three hidden layers, each containing a variable number of nodes.

   - Used the ReLU activation function and the Adam optimizer.

   - Trained the model over 500 iterations.

3. **Model Evaluation**:

   - Evaluated the model on the test set.

   - Measured accuracy for different numbers of nodes in the hidden layers (3 to 9 nodes per layer).

---

## Code Implementation

### Core Steps:

```python

# Import libraries

import pandas as pd

from sklearn.neural_network import MLPClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# Load dataset

df = pd.read_csv('diabetes.csv')

print("Dataset columns:", df.columns.tolist())

# Normalize features

x_columns = df.columns.tolist()

x_columns.pop()  # Remove the target column

df[x_columns] = df[x_columns] / df[x_columns].max()

# Extract features (X) and labels (y)

X = df[x_columns].values

y = df[['Outcome']].values.ravel()

# Split dataset

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=40)

# Build and train the MLP classifier

mlp = MLPClassifier(hidden_layer_sizes=(6, 6, 6), activation='relu', solver='adam', max_iter=500)

mlp.fit(X_train, y_train)

# Test accuracy

predict_test = mlp.predict(X_test)

test_accuracy = accuracy_score(y_test, predict_test) * 100

print("Accuracy on test data = %.2f" % test_accuracy)