Comparing Naive Bayes Method and Artificial Neural Network for Semen Quality Categorization

Abstract

One of the world wide health care concerns in the last two decades has been the decrease in fertility rates. The problem is said to be more severe among the male population. Research has shown that environmental factors and life style habits have an impact on the quality of semen. Orthodox diagnosis of seminal quality employs a laboratory approach, involving expensive tests, which are also sometimes uncomfortable to the patient. Application of machine learning techniques has been on the rise and has demonstrated encouraging results in many fields, including health care. In this paper we propose Naïve Bayes and Artificial Neural Network classifiers for the characterization of seminal quality, based on environmental factors and life style habits Comparisons between the two classifier models show that their accuracy rate is the same and stands at 97%, on the training set.