Seminal quality evaluation with RBF neural network

Abstract

The orthodox system of diagnosis in medicine requires that laboratory procedures should be performed to obtain the results of diagnostic queries. While this practice is standard, the _eld of machine learning is now revolutionizing medical diagnoses. Data (medical histories) of different parents archived over long periods can be used to make predictions on new cases with reasonable accuracies using suitable machine learning methods. Moreover, in many instances where the current situations of patients do not yet justify the cost of expensive laboratory test procedures, machine learning methods can be used to learn past medical data, with which new cases can be diagnosed. Moreover, the cost is more reasonable and justi_able with this approach. In this work, we apply a radial basis function neural network to the evaluation of semen quality (viability) using some attributes relating to the patients. Important parameters used to assess the performance of the considered model include precision, recall, accuracy, and F1 score based on a 10-fold cross-validation scheme.