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

机译:决策树和K-Nn的比较分析了2种不同滤波器的生育力

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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.
机译:生育能力对于已婚夫妇为期年龄和今天的重要临床问题是一个重要的问题。仅限妇女在生育调节中有过分的责任负担,而且男性不孕症是男性不孕症的一些主要原因和危险因素,即环境因素,生活方式因素等。我们将使用两个算法,决策树和k最近的邻居分析不孕症。我们将使用不同的训练数据和不同滤波器进行实验。目的是确定哪种算法更准确使用过滤器的2种算法之间。结果显示DT在使用DataSet而无需过滤器时具有更好的性能,并且在使用重新取滤器时使用随机滤波器时具有更好的性能。

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