<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Cancer Detection</title>
</head>
<body>
<h1>Cancer Detection</h1>
<div>
<label for="feature1">Feature 1:</label>
<input type="number" id="feature1" step="any"><br>
<label for="feature2">Feature 2:</label>
<input type="number" id="feature2" step="any"><br>
<label for="feature3">Feature 3:</label>
<input type="number" id="feature3" step="any"><br>
<label for="feature4">Feature 4:</label>
<input type="number" id="feature4" step="any"><br>
<label for="feature5">Feature 5:</label>
<input type="number" id="feature5" step="any"><br>
<label for="feature6">Feature 6:</label>
<input type="number" id="feature6" step="any"><br>
<label for="feature7">Feature 7:</label>
<input type="number" id="feature7" step="any"><br>
<label for="feature8">Feature 8:</label>
<input type="number" id="feature8" step="any"><br>
<label for="feature9">Feature 9:</label>
<input type="number" id="feature9" step="any"><br>
<label for="feature10">Feature 10:</label>
<input type="number" id="feature10" step="any"><br>
<label for="feature11">Feature 11:</label>
<input type="number" id="feature11" step="any"><br>
<label for="feature12">Feature 12:</label>
<input type="number" id="feature12" step="any"><br>
<label for="feature13">Feature 13:</label>
<input type="number" id="feature13" step="any"><br>
<label for="feature14">Feature 14:</label>
<input type="number" id="feature14" step="any"><br>
<label for="feature15">Feature 15:</label>
<input type="number" id="feature15" step="any"><br>
<button onclick="detectCancer()">Detect Cancer</button>
</div>
<div id="result"></div>
<script>
function detectCancer() {
const features = [];
for (let i = 1; i <= 15; i++) {
features.push(parseFloat(document.getElementById('feature' + i).value));
}
// Example ML model (replace with your actual model)
const prediction = predictCancer(features);
let resultText = '';
if (prediction === 1) {
resultText = 'Cancer detected.';
} else {
resultText = 'No cancer detected.';
}
document.getElementById('result').innerText = resultText;
}
function predictCancer(features) {
// Example: Simple rule-based model (replace with your actual model)
// This example predicts cancer if the sum of the features is greater than 50
const sum = features.reduce((acc, curr) => acc + curr, 0);
return sum > 50 ? 1 : 0;
}
</script>
</body>
</html>
// Define a function to preprocess the input data
function preprocessData(data) {
// Implement data preprocessing steps (e.g., normalization, feature scaling)
return preprocessedData;
}
// Define a function to load the machine learning model
function loadModel() {
// Load the pre-trained machine learning model (e.g., TensorFlow.js model)
return model;
}
// Define a function to predict cancer based on input features
function predictCancer(model, features) {
// Use the loaded model to predict cancer based on the input features
return prediction;
}
// Define a function to display the prediction result
function displayResult(prediction) {
// Display the prediction result to the user
}
// Example: Generate 180 lines of code for data collection, feature extraction, and prediction
// This is a simplified example and should be replaced with actual implementation
// Data collection
let data = /* Collect data from the user or external sources */;
// Preprocess the data
let preprocessedData = preprocessData(data);
// Load the machine learning model
let model = loadModel();
// Predict cancer based on the input features
let prediction = predictCancer(model, preprocessedData);
// Display the prediction result
displayResult(prediction);
// Repeat the above steps for each new data point or input
// Note: This example is simplified and does not include actual machine learning model training or validation