🧠 SleepWell: A Machine Learning Web App for Early Detection of Sleep Disorders

SleepWell is a Laravel-powered web application designed to support the early detection of sleep disorders through machine learning. It integrates a trained Random Forest Classifier served via Flask to deliver fast, interpretable predictions from basic health and lifestyle indicators.

With an intuitive user interface and a Laravel-managed admin panel, the system enables both users and administrators to interact with predictions and analyze behavioral trends securely and efficiently.

🔍 Core Features

• Real-time prediction of potential sleep disorders based on inputs like age, gender, daily steps, and occupation.
• Seamless integration of a Python-based ML model into a modern Laravel frontend.
• RESTful API endpoint (/predictsleep) accepting structured input and returning categorical predictions.
• Secure admin panel for overseeing user submissions, logs, and inference history.

🧪 Model Performance

The Random Forest Classifier was trained on a curated dataset with the following performance:

Accuracy  : 0.87
Precision : 0.86 (class 0), 0.88 (class 1)
Recall    : 0.86 (class 0), 0.88 (class 1)
F1-Score  : 0.87

This reflects a strong balance between sensitivity and specificity—critical for early detection use cases.

🛠️ Tech Stack

• Backend ML: Python, Flask, Scikit-learn, Pandas, Joblib
• Web Application: Laravel, MySQL, Blade Templates
• Visualization: Matplotlib
• Storage & Deployment: Structured dataset ingestion, model serialization, and API consumption
• Data Pipeline: Label Encoding, Random Forest Classification, Test-Train Split, JSON I/O

🧬 Key Training Snippet

X = df[['Age', 'Gender', 'DailySteps', 'Occupation']]
y = df['Sleep Disorder']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = RandomForestClassifier()
model.fit(X_train, y_train)
joblib.dump(model, 'rf_sleep_model.pkl')

Prediction logic (served from Flask endpoint):

@app.route('/predictsleep', methods=['POST'])
def predict_sleep():
    # Input parsing, label encoding, prediction, and response

SleepWell bridges data science and healthcare accessibility, offering a scalable solution that encourages proactive wellness decisions using machine intelligence.

🧠 Model training source:

🌐 Web application source: