AI-Driven Sleep Health Recommendations Using Deep Learning for Personalized Medicine

Improving a person’s general health depends on getting enough sleep, but getting personalized recommendations is difficult. To provide personalized recommendations for sleep health, this research introduces an AI-driven method that uses Long Short-Term Memory (LSTM) networks. Wearable sleep monitoring devices collect complicated time-series data, which is analyzed using LSTM. The model creates personalized recommendations to enhance sleep quality and control sleep disorders by collecting and analyzing complex temporal patterns in sleep behavior. As part of the research, the LSTM model is trained on large datasets of sleep variables, including duration, quality, and challenges. The accuracy of the model’s sleep pattern predictions and its capacity to provide practical recommendations are used to evaluate its performance. This method not only makes sleep health treatments more accurate but also makes personalized recommendations to the specific needs of each user by analyzing their sleep patterns. By providing personalized insights and recommendations, the results showcase the potential of LSTM networks to transform the field of sleep health management. Enhances personalized medicine to the varied and ever-changing requirements of people aiming for improved sleep health.