The comparison of decision tree and k-NN to analyze fertility using 2 different filters

Fertility is a crucial issue for married couples for ages and a significant clinical problem today. Not only do women have an undue burden of responsibility in fertility regulation but also men. There are some major causes and risk factors for male infertility i.e., environmental factors, life style factors etc. In this paper, we will analyse the infertility using two algorithms, decision tree and k-nearest neighbours. We will do experiments using different splits of training data and different filters. The purpose is to determine which algorithm is more accurate between 2 algorithms either using filter or not. The result shows DT has better performance in accuracy when using dataset without filter and when using randomize filter while k-NN has better performance when using resample filter.