A Novel Meta-Analysis based Predicting Toxicity in Meditation Practices using Optimize Deep Learning

The main goal of this research work is to predict medication toxicity with the help of deep learning models and consider opioid prescriptions only. As the dataset used has 25k records and 256 features, Recurrent Neural Networks (RNN), Random Forest, XGBoost, and Voting Classifier increase the chances of prediction. To enhance the feature space in the dataset, various features regarding medications that are frequently prescribed include Acetaminophen, Gabapentin and Levothyroxine among others, the target feature is known as the ‘Opioid Prescriber’. Intending to detect whether a prescriber is subscribing to opioids and to minimize the threats related to opioid toxicity, this work tries to ascertain associative structures among distinct medications. The integration of machine learning and deep learning techniques will form a strong foundation, so further enhancing prescription safety and eliminating opioid toxicities. The accurate implementation in python 3.10 of using deep learning algorithm results getting high accuracy RNNis is 98%, XGBoost is 94%, Voting Classifier is 87%, Random Forest is 84%.