Naman Srivastava

Posted on Aug 10 • Edited on Sep 21

Diabetes Detection On AWS

#aws #webdev #machinelearning #programming

Diabetes Detection on AWS — Step-by-Step Complete Guide

A practical, step-by-step walkthrough to build a production-ready Diabetes Prediction web app using AWS services. Manual setup through AWS Console with code files for EC2 deployment.

TL;DR (One-line)

Train a scikit-learn model locally → upload to S3 via console → create SNS topic & DynamoDB table via console → host Flask API on EC2 that loads model from S3, predicts & stores results → expose via API Gateway → host frontend with AWS Amplify.

GitHub reference: https://github.com/naman-0804/Diabetes_Prediction_onAWS

Architecture Overview

Local Training — Train model locally with scikit-learn
S3 — Store model file (modelaws.joblib)
EC2 + Flask — Backend API server
DynamoDB — Store prediction results
SNS — Email notifications to users
API Gateway — Secure, managed public endpoint
Amplify — Frontend hosting

Prerequisites

AWS account with permissions
Python 3.8+ locally
WinSCP for file transfer to EC2: https://winscp.net/eng/download.php
PuTTY for SSH access
Basic knowledge of Flask and AWS console

Manual Setup Steps

Step 1 — Train Model Locally

Create train.py:

# train.py - Train diabetes prediction model
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import joblib

# Load dataset (download from Kaggle: Pima Indian Diabetes Dataset)
df = pd.read_csv('diabetes.csv')

X = df.drop('Outcome', axis=1)
y = df['Outcome']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Save model
joblib.dump(model, 'modelaws.joblib')
print("Model saved as modelaws.joblib")

# Test accuracy
accuracy = model.score(X_test, y_test)
print(f"Model accuracy: {accuracy:.2f}")

Run locally:

python train.py

This creates modelaws.joblib file.

Step 2 — Manual AWS Console Setup

2.1 Create S3 Bucket

Go to AWS Console → S3
Click "Create bucket"
Choose unique bucket name (e.g., your-diabetes-model-bucket)
Select your region (e.g., us-east-1)
Keep default settings, click "Create bucket"
Upload modelaws.joblib to this bucket

2.2 Create SNS Topic

Go to AWS Console → SNS
Click "Create topic"
Choose "Standard" type
Name: diabetes-predictions
Click "Create topic"
Copy the Topic ARN (you'll need this)
Click "Create subscription"
Protocol: Email
Endpoint: your email address
Check your email and confirm subscription

2.3 Create DynamoDB Table

Go to AWS Console → DynamoDB
Click "Create table"
Table name: DiabetesPredictions
Partition key: email (String)
Sort key: timestamp (String)
Use default settings
Click "Create table"

2.4 Create IAM Role for EC2

Go to AWS Console → IAM
Click "Roles" → "Create role"
Select "AWS service" → "EC2"
Click "Next"
Create custom policy with this JSON:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["s3:GetObject"],
      "Resource": ["arn:aws:s3:::your-diabetes-model-bucket/*"]
    },
    {
      "Effect": "Allow",
      "Action": ["sns:Publish", "sns:Subscribe", "sns:ListSubscriptionsByTopic"],
      "Resource": ["arn:aws:sns:us-east-1:YOUR-ACCOUNT-ID:diabetes-predictions"]
    },
    {
      "Effect": "Allow",
      "Action": ["dynamodb:PutItem", "dynamodb:UpdateItem", "dynamodb:GetItem"],
      "Resource": ["arn:aws:dynamodb:us-east-1:YOUR-ACCOUNT-ID:table/DiabetesPredictions"]
    }
  ]
}

Name the role: diabetes-ec2-role
Click "Create role"

Step 3 — Launch EC2 Instance

Go to AWS Console → EC2
Click "Launch instance"
Choose "Amazon Linux 2 AMI"
Instance type: t2.micro (free tier)
Important: In "Advanced details" → "IAM instance profile" → select diabetes-ec2-role
Security Group settings:
- SSH (22) from your IP
- HTTP (80) from anywhere
- Custom TCP (5000) from anywhere (for testing)
Create/use existing key pair
Launch instance

Step 4 — Code Files to Upload

Create these files on your local machine:

backend.py

import json
import boto3
import pandas as pd
import joblib
from flask import Flask, request, jsonify
from flask_cors import CORS
from datetime import datetime

app = Flask(__name__)
CORS(app)

# Configuration - UPDATE THESE VALUES
BUCKET_NAME = 'your-diabetes-model-bucket'  # Your S3 bucket name
MODEL_KEY = 'modelaws.joblib'  # Your model file name in S3
SNS_TOPIC_ARN = 'arn:aws:sns:us-east-1:YOUR-ACCOUNT-ID:diabetes-predictions'  # Your SNS topic ARN
DYNAMODB_TABLE_NAME = 'DiabetesPredictions'  # Your DynamoDB table name
AWS_REGION = 'us-east-1'  # Your AWS region

# Initialize AWS clients
s3 = boto3.client('s3', region_name=AWS_REGION)
sns_client = boto3.client('sns', region_name=AWS_REGION)
dynamodb = boto3.resource('dynamodb', region_name=AWS_REGION)
table = dynamodb.Table(DYNAMODB_TABLE_NAME)

# Download and load the model when the server starts
model = None
download_path = '/tmp/modelaws.joblib'

try:
    s3.download_file(BUCKET_NAME, MODEL_KEY, download_path)
    model = joblib.load(download_path)
    print("Model loaded successfully from S3")
except Exception as e:
    print(f"Error loading model: {str(e)}")

@app.route('/', methods=['GET'])
def health_check():
    return jsonify({'status': 'API is live'})

@app.route('/predict', methods=['POST'])
def predict():
    if not model:
        return jsonify({'error': 'Model could not be loaded'}), 500

    try:
        # Get JSON data from the request
        data = request.get_json()
        if not data:
            return jsonify({'error': 'Invalid or missing JSON data'}), 400

        email = data.get('email')
        if not email:
            return jsonify({'error': 'Email is required'}), 400

        # Extract input data with defaults
        pregnancies = int(data.get('Pregnancies', 0))
        glucose = int(data.get('Glucose', 0))
        blood_pressure = int(data.get('BloodPressure', 0))
        skin_thickness = int(data.get('SkinThickness', 0))
        insulin = int(data.get('Insulin', 0))
        bmi = float(data.get('BMI', 0.0))
        pedigree = float(data.get('DiabetesPedigreeFunction', 0.0))
        age = int(data.get('Age', 0))

        # Check if email is already subscribed to SNS
        try:
            response = sns_client.list_subscriptions_by_topic(TopicArn=SNS_TOPIC_ARN)
            subscriptions = response.get('Subscriptions', [])
            if not any(sub['Endpoint'] == email for sub in subscriptions):
                sns_client.subscribe(
                    TopicArn=SNS_TOPIC_ARN,
                    Protocol='email',
                    Endpoint=email
                )
        except Exception as e:
            print(f"Error subscribing email: {e}")

        # Prepare input data for prediction
        input_data = [[pregnancies, glucose, blood_pressure, skin_thickness,
                       insulin, bmi, pedigree, age]]
        input_df = pd.DataFrame(input_data, columns=[
            'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',
            'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'
        ])

        # Make prediction
        prediction = model.predict(input_df)
        predicted_label = int(prediction[0])

        # Send SNS notification
        message = f"Diabetes Prediction Result: {predicted_label}"
        try:
            sns_client.publish(
                TopicArn=SNS_TOPIC_ARN,
                Message=message,
                Subject='Diabetes Prediction Result'
            )
        except Exception as e:
            print(f"Error sending SNS: {e}")

        # Store result in DynamoDB
        result_data = {
            'email': email,
            'timestamp': datetime.utcnow().isoformat(),
            'Pregnancies': pregnancies,
            'Glucose': glucose,
            'BloodPressure': blood_pressure,
            'SkinThickness': skin_thickness,
            'Insulin': insulin,
            'BMI': bmi,
            'DiabetesPedigreeFunction': pedigree,
            'Age': age,
            'PredictedOutcome': predicted_label
        }

        try:
            table.put_item(Item=result_data)
        except Exception as e:
            print(f"Error storing in DynamoDB: {e}")

        return jsonify({'Predicted Label': predicted_label})

    except Exception as e:
        print(f"Error in prediction: {str(e)}")
        return jsonify({'error': str(e)}), 400

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=False)

requirements.txt

flask==2.3.3
flask-cors==4.0.0
boto3==1.28.57
pandas==2.1.1
scikit-learn==1.3.0
joblib==1.3.2
gunicorn==21.2.0

index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Diabetes Prediction</title>
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }

        body {
            font-family: 'Arial', sans-serif;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            min-height: 100vh;
            display: flex;
            justify-content: center;
            align-items: center;
        }

        .container {
            background: white;
            padding: 2rem;
            border-radius: 15px;
            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
            width: 100%;
            max-width: 500px;
        }

        h2 {
            text-align: center;
            color: #333;
            margin-bottom: 2rem;
            font-size: 2rem;
        }

        .input-container {
            margin-bottom: 1rem;
        }

        label {
            display: block;
            margin-bottom: 0.5rem;
            color: #555;
            font-weight: bold;
        }

        input {
            width: 100%;
            padding: 12px;
            border: 2px solid #ddd;
            border-radius: 8px;
            font-size: 16px;
            transition: border-color 0.3s;
        }

        input:focus {
            outline: none;
            border-color: #667eea;
        }

        button {
            width: 100%;
            padding: 15px;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            border: none;
            border-radius: 8px;
            font-size: 18px;
            font-weight: bold;
            cursor: pointer;
            transition: transform 0.2s;
            margin-top: 1rem;
        }

        button:hover {
            transform: translateY(-2px);
        }

        #result {
            margin-top: 2rem;
            padding: 1rem;
            border-radius: 8px;
            text-align: center;
            font-weight: bold;
            min-height: 50px;
            display: flex;
            align-items: center;
            justify-content: center;
        }

        .success {
            background-color: #d4edda;
            color: #155724;
            border: 1px solid #c3e6cb;
        }

        .error {
            background-color: #f8d7da;
            color: #721c24;
            border: 1px solid #f5c6cb;
        }
    </style>
</head>
<body>

    <div class="container">
        <h2>🩺 Diabetes Prediction</h2>
        <form id="predictionForm">
            <div class="input-container">
                <label for="email">📧 Email:</label>
                <input type="email" id="email" placeholder="Enter your email" required>
            </div>

            <div class="input-container">
                <label for="Pregnancies">🤱 Pregnancies:</label>
                <input type="number" id="Pregnancies" placeholder="Enter number of pregnancies" required min="0">
            </div>

            <div class="input-container">
                <label for="Glucose">🍬 Glucose Level:</label>
                <input type="number" id="Glucose" placeholder="Enter glucose level (mg/dL)" required min="0">
            </div>

            <div class="input-container">
                <label for="BloodPressure">💓 Blood Pressure:</label>
                <input type="number" id="BloodPressure" placeholder="Enter blood pressure (mmHg)" required min="0">
            </div>

            <div class="input-container">
                <label for="SkinThickness">📏 Skin Thickness:</label>
                <input type="number" id="SkinThickness" placeholder="Enter skin thickness (mm)" required min="0">
            </div>

            <div class="input-container">
                <label for="Insulin">💉 Insulin Level:</label>
                <input type="number" id="Insulin" placeholder="Enter insulin level (μU/mL)" required min="0">
            </div>

            <div class="input-container">
                <label for="BMI">⚖️ BMI:</label>
                <input type="number" step="0.1" id="BMI" placeholder="Enter BMI (kg/m²)" required min="0">
            </div>

            <div class="input-container">
                <label for="DiabetesPedigreeFunction">🧬 Diabetes Pedigree Function:</label>
                <input type="number" step="0.01" id="DiabetesPedigreeFunction" placeholder="Enter DPF (0.0 - 2.5)" required min="0" max="2.5">
            </div>

            <div class="input-container">
                <label for="Age">🎂 Age:</label>
                <input type="number" id="Age" placeholder="Enter age in years" required min="1" max="120">
            </div>

            <button type="button" onclick="submitData()">🔮 Get Prediction</button>
        </form>
        <div id="result"></div>
    </div>

    <script>
        function submitData() {
            const resultElement = document.getElementById("result");
            resultElement.innerText = "🔄 Processing your prediction...";
            resultElement.className = "";

            const data = {
                email: document.getElementById("email").value,
                Pregnancies: Number(document.getElementById("Pregnancies").value),
                Glucose: Number(document.getElementById("Glucose").value),
                BloodPressure: Number(document.getElementById("BloodPressure").value),
                SkinThickness: Number(document.getElementById("SkinThickness").value),
                Insulin: Number(document.getElementById("Insulin").value),
                BMI: parseFloat(document.getElementById("BMI").value),
                DiabetesPedigreeFunction: parseFloat(document.getElementById("DiabetesPedigreeFunction").value),
                Age: Number(document.getElementById("Age").value),
            };

            // Update this URL to your API Gateway endpoint or EC2 public IP
            const API_URL = 'http://YOUR-EC2-PUBLIC-IP:5000/predict';

            fetch(API_URL, {
                method: 'POST',
                headers: { 'Content-Type': 'application/json' },
                body: JSON.stringify(data)
            })
            .then(response => {
                if (!response.ok) {
                    throw new Error('Network response was not ok: ' + response.statusText);
                }
                return response.json();
            })
            .then(result => {
                const prediction = result['Predicted Label'];
                const message = prediction === 1 
                    ? '⚠️ Higher risk detected. Please consult a healthcare professional.' 
                    : '✅ Lower risk detected. Continue maintaining a healthy lifestyle.';

                resultElement.innerHTML = `
                    <strong>Prediction Result: ${prediction}</strong><br>
                    ${message}<br>
                    📧 A detailed report has been sent to your email.
                `;
                resultElement.classList.add(prediction === 1 ? "error" : "success");
            })
            .catch(error => {
                console.error('Error:', error);
                resultElement.innerText = '❌ Error: ' + error.message;
                resultElement.classList.add("error");
            });
        }
    </script>
</body>
</html>

Step 5 — Deploy to EC2

5.1 Connect to EC2

Use PuTTY to connect to your EC2 instance
Use the .ppk key file and EC2 public IP

5.2 Prepare EC2 Environment

# Update system
sudo yum update -y

# Install Python 3 and pip
sudo yum install -y python3 python3-pip

# Create app directory
mkdir ~/app
cd ~/app

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Update pip
pip install --upgrade pip

5.3 Upload Files using WinSCP

Open WinSCP
Connect to your EC2 instance using .ppk key
Upload these files to /home/ec2-user/app/:
- backend.py
- requirements.txt
- index.html

5.4 Install Dependencies and Run

# Navigate to app directory
cd ~/app
source venv/bin/activate

# Install requirements
pip install -r requirements.txt

# Update backend.py with your actual values:
# - BUCKET_NAME
# - SNS_TOPIC_ARN (from Step 2.2)
# - AWS region and account ID

# Run the application
python backend.py

Step 6 — Create API Gateway (Optional)

Go to AWS Console → API Gateway
Create REST API
Create resource /predict
Create POST method
Integration type: HTTP
Endpoint URL: http://your-ec2-public-ip:5000/predict
Enable CORS
Deploy API
Update index.html with the API Gateway URL

Step 7 — Test Your Application

Test Backend Directly:

curl -X POST http://your-ec2-public-ip:5000/predict \
  -H "Content-Type: application/json" \
  -d '{
    "email": "test@example.com",
    "Pregnancies": 2,
    "Glucose": 120,
    "BloodPressure": 70,
    "SkinThickness": 20,
    "Insulin": 79,
    "BMI": 25.5,
    "DiabetesPedigreeFunction": 0.5,
    "Age": 33
  }'

Test Frontend:

Open index.html in browser
Fill out the form
Click "Get Prediction"
Check email for SNS notification
Check DynamoDB table for stored result

Step 8 — Deploy Frontend with Amplify

Create GitHub repository
Upload index.html to repository
Go to AWS Console → Amplify
Connect to GitHub
Deploy your repository

Troubleshooting

Common Issues:

Model not loading from S3
- Check bucket name and file name in backend.py
- Verify IAM role has S3 read permissions
SNS emails not received
- Check spam folder
- Confirm email subscription in SNS console
DynamoDB permission errors
- Verify IAM role has DynamoDB write permissions
- Check table name in backend.py
CORS errors
- Enable CORS in API Gateway
- Check flask-cors is installed
Connection refused errors
- Check EC2 security group allows port 5000
- Verify application is running on correct port

Files Summary

You need these 3 main files:

backend.py - Flask API server
requirements.txt - Python dependencies
index.html - Frontend form

Transfer to EC2 using WinSCP, install dependencies, update configuration values, and run!

Final Notes

Always use IAM roles instead of hardcoded AWS keys
Consider using Elastic IP for stable EC2 endpoint
Enable CloudWatch logging for monitoring
Use HTTPS in production with SSL certificate
Implement input validation and rate limiting for production use

This guide provides a complete manual setup process using AWS Console with minimal CLI usage. The architecture is production-ready and follows AWS best practices.

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