Sleep Disorder Prediction using Supervised Machine Learning

Description of the Project

This project uses a Supervised Machine Learning algorithm (Random Forest) to predict and identify sleep disorders such as sleep apnea and insomnia. It uses existing data and data analysis techniques to categorize sleep-related metrics.

Key Features of this Project

• Automated prediction of sleep disorders based on lifestyle factors
• Uses comprehensive sleep data, lifestyle factors and cardiovascular health indicators
• Enhances and validates existing sleep disorder prediction measures through AI measures

AI Utilization in this Project

1. Data Analysis and Pattern Recognition: The Random Forest algorithm analyzes diverse data to uncover hidden patterns and correlations between different factors influencing sleep disorders
2. Model Training: The Supervised Machine Learning predicts the likelihood of sleep disorder based on input data
3. Model Evaluation and Optimization: Use of sci-kit learn and TensorFlow library to optimize the model

Supervised Machine Learning

• Trains the model based on labeled data
• Preprocesses the data

Value of this Project

1. Educational Tool: This project demonstrates the real world application of AI
2. Medical Field: Helps in identifying sleep disorders
3. Conceptual Understanding: Foundation for more complex AI models

Computing Resources required for this Project

The project can be executed on modern computers or laptops and no additional hardware is required.

Technologies Used

• Python
• Sci-kit learn
• TensorFlow