An efficient Apriori algorithm for frequent pattern in human intoxication data

Addicts and non-addicts can be distinguished by analyzing social behaviors and activities. An attempt was made in the study to discover the main rules that cause people to get hooked. We utilized an open-source dataset with 474 total instances and 212 total addicted individuals. They asked 50 questions during the data collection process. All of the questions were created using the Index of Addiction Severity and with the assistance of drug addiction psychologists. In this study, we utilized the Apriori algorithm to extract the most important rules from the dataset. By following this guideline, it will be clear whether or not someone is hooked based on their social conduct. The Apriori algorithm was used to find rules from the dataset, and eight significant rules were discovered, with a confidence level of 95% and a support level of 45%.