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Algorithms 2014, 7(3), 405-417;

Seminal Quality Prediction Using Clustering-Based Decision Forests

School of Mathematics & Statistics, Central South University, Changsha, Hunan, 410075, China
Author to whom correspondence should be addressed.
Received: 14 May 2014 / Revised: 23 June 2014 / Accepted: 29 July 2014 / Published: 11 August 2014
PDF [441 KB, uploaded 11 August 2014]


Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal quality prediction faces the class imbalance problem. In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests, to tackle unbalanced class learning problem in seminal quality prediction. Experiment results on real fertility diagnosis dataset have shown that Clustering-Based Decision Forests outperforms decision tree, Support Vector Machines, random forests, multilayer perceptron neural networks and logistic regression by a noticeable margin. Clustering-Based Decision Forests can also be used to evaluate variables’ importance and the top five important factors that may affect semen concentration obtained in this study are age, serious trauma, sitting time, the season when the semen sample is produced, and high fevers in the last year. The findings could be helpful in explaining seminal concentration problems in infertile males or pre-screening semen donor candidates. View Full-Text
Keywords: seminal prediction; imbalanced learning; variable importance seminal prediction; imbalanced learning; variable importance
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Wang, H.; Xu, Q.; Zhou, L. Seminal Quality Prediction Using Clustering-Based Decision Forests. Algorithms 2014, 7, 405-417.

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