Comparison study of email classifications for healthcare organizations

At present, email is one of the primary communication tools for industrial organizations and government agencies. The email system is popular, powerful, and efficient but has some shortcomings, as it contains unstructured data and spam emails. Each organization receives a profound number of emails each day, and dedicated resources are needed to process them. The allocated resources are the cost of the company. Spam emails add unnecessary workload to the company and affect the company´s performance economically. Spam emails may have different characteristics in different industrial sectors. Healthcare organizations such as hospitals, clinics, and retirement centers receive spam emails with different features from other companies. This paper discusses the previous spam filters and describes a hybrid approach with machine learning algorithms and association rules to address the spam emails with specific characteristics in the healthcare system.