IMPROVE MACHINE LEARNING RESULTS FOR SEMEN ANALYSIS USING ENSEMBLE META CLASSIFICATION

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Published: Oct 30, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i8.4755
Keywords:
Semen analysis, Machine Learning, Meta Classifier, Ensemble Classification, Decision Tree classification, Naïve-Bayesian Classification, IBK.

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priya natrajan
SDNB Vaishnav College for Women, Chromepet, Chennai-600044

Abstract

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.

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Vol. 8 No. 8 (2017): September-October
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Articles

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References

David Gil, Jose Luis Girela et.al, †Predicting seminal quality with artificial intelligence methodsâ€, Expert Systems with Applications, Vol. 39(16),pp. 12564–12573, 2012.

Macmillan Simfukwe et.al, “Comparing Naive Bayes Method and Artificial Neural Network for Semen Quality Categorization “, International Journal of Innovative Science, Engineering & Technology, Vol. 2(7), pp. 689-694, 2015.

Thomas G. Dietterich, â€An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomizationâ€, Vol. 40,pp. 139-157, 2000.

Quinlan, J. R. , â€Bagging, boosting, and C4.5â€, In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 725–730, 1996.

Barrat C.L., Clements S., Kessopoulou E, †Semen Characteristics and fertility tests required for storage of spermatozoaâ€, Human Reproduction (Oxford, England), vol. 13(Suppl. 2). pp. 1-11, 1998.

Koletti P.N, â€Evaluation of the subfertile manâ€, American Family Physician, vol. 67 (10), pp. 2165-2172, 2003.

WHO, WHO laboratory manual for the examination of human semen and sperm-cervical mucus interaction (4th ed.). Published on behalf of the World Health Organization by Cambridge University Press, Cambridge, UK, 1999.

Lisboa P.J, Taktak A.F.G, “The use of artificial neural networks in decision support in cancer: A systematic reviewâ€, Neural Networks, vol. 19(4), pp. 408-425, 2006.