Sleep Disorder Classification Using Random Forest
A machine learning system for predicting sleep disorders using everyday health and lifestyle factors.
95.2%
Model Accuracy
3
Disorder Categories
11
Health Parameters
400+
Training Samples
Common Sleep Disorders
Understanding the distinct patterns and health impacts of primary sleep conditions.
Insomnia
Difficulty falling asleep or staying asleep, leading to poor sleep quality and daytime fatigue.
Sleep Apnea
Breathing repeatedly stops and starts during sleep, significantly disrupting restorative sleep cycles.
Sleep Deprivation
Condition of not having enough sleep, which can be acute or chronic, affecting cognitive function.
Powered by Random Forest
Our classification system utilizes a robust Random Forest machine learning model. By constructing a multitude of decision trees at training time, it provides high accuracy and minimizes overfitting when analyzing complex health variables.
- High diagnostic accuracy
- Handles non-linear parameters
- Provides feature importance insights
- Robust against missing values