Identification of Adverse Event Patterns in Loperamide Using FP-Growth Algorithm

Abstract

Loperamide is a drug to treat diarrhea, and should not be used for self-medication. There are cases of cardiac disorders due to the use of this drug, this is called an adverse event. Based on this problem, this research will identify the adverse event pattern in Loperamide using the FP-Growth algorithm. The data used is FAERS data from 2015-2017 with the attributes is the age group, sex, drug name, and adverse event. The amount of data after the preprocessing process is 2,840 records. The results is we found the pattern with the highest support values is a combination of adult age groups, male sex, the drug name Loperamide and adverse event cardiac disorders that have a support value of 3.8%, confidence 28.35% and lift ratio 1.483. The implemented using Matlab software and tested using SPMF tools.