Determine the Effect Hookah Smoking on Health with Different Types of Tobacco by using Parallel Processing Technique

Abstract

Abstract In this paper, we design efficient neural network as classification problem to examine the perception of health risks of different types of tobacco use in comparison to hookah use among 600 participants respond to a survey conducted in Baghdad city, who never smoke or smoke cigarette, hookah, both cigarette and hookah; and determine whether smoking status influenced the perceived health risks of hookah smoking when compared to other forms of tobacco use. The input data for suggested design is obtained from survey had 30 questions including questions about demographics, socioeconomic status, health care, etc. the network training based on numerical optimization procedure. This technique is much faster as compared to traditional approaches and it also provides highly accurate results. This study found that 48.1%of the participants believed that hookah was less harmful than each of the other tobacco products studied. Approximately 26% of cigarette smokers are likely to smoke hookah also. These findings emphasize the importance of spreading awareness about the health risks associated with hookah smoking and implementing strict laws for showing tobacco-related warnings in hookah advertisements and on the packets of hookah accessories given the increasing popularity of hookah, especially among the youth, in the Baghdad city.