IMPROVE MACHINE LEARNING RESULTS FOR SEMEN ANALYSIS USING ENSEMBLE META CLASSIFICATION
N. Priya · International Journal of Advanced Research in Computer Science · 2017
Numerous studies have found that, declining male fertility around the world in the past 50 years are still falling at a rate of two per cent every year. Reasons for this decline range from our increasingly stressful lifestyles, poor diet, drinking and smoking, or a lack of exercise and environmental factors. Applications of machine learning techniques have been implemented in many fields, including health care. This paper have experimented ensemble meta classification techniques such as bagging, boosting and stacking for improving the performance of the Machine Learning algorithms such as Decision-tree (J48), IBK and Naïve-Bayesian (NB) classification for the characterization of seminal quality. The performance of general machine learning classifier is compared with ensemble classifier models and also been verified with their accuracy and error rate.