Prediction of semen quality using artificial neural network

Area of Science:

• Reproductive Medicine
• Artificial Intelligence in Healthcare
• Biostatistics

Background:

• Semen analysis is crucial for male fertility assessment and sperm donor screening.
• Preliminary evaluation of semen characteristics can be enhanced by predictive tools.
• Artificial neural networks (ANNs) offer potential for analyzing complex biological data.

Purpose of the Study:

• To develop and evaluate artificial neural network models for predicting semen analysis outcomes.
• To assess the feasibility of using basic questionnaire data for semen profile prediction.
• To determine the efficacy of ANNs in classifying semen analysis results.

Main Methods:

• Development of two ANN models based on eleven survey questions.
• Model 1: Prediction of overall semen performance and profile.
• Model 2: Prediction of sperm concentration, with performance evaluation using learning and test sets.

Main Results:

• The ANN model for predicting sperm concentration demonstrated high efficiency.
• Achieved 92.93% accuracy in the learning set and 85.71% in the test set for classifying semen analysis results.
• The developed models show potential for accurate preliminary semen evaluation.

Conclusions:

• Artificial neural networks can serve as a valuable tool for the preliminary assessment of semen characteristics.
• Questionnaire-based ANN models may aid in predicting semen profile and sperm concentration.
• This approach could streamline initial fertility investigations and sperm donor candidate evaluations.