Comparative Analysis of Predictive Models for the Likelihood of Infertility in Women Using Supervised Machine Learning Techniques

Infertility is a worldwide problem, affecting 8% – 15% of the couples in their reproductive age. WHO estimates that there are 60 - 80 million infertile couples worldwide with the highest incidence in some regions of Sub-Saharan Africa also infertility rate may reach 50% compared to 20% in Eastern Mediterranean Region and 11% in the developed world. Infertility has caused considerable social, emotional and psychological stress between couples, among families, within the individual concerned and the society at large. Historical data constituting information describing the risk factors of infertility alongside the respective infertility likelihood status of women was collected from Obafemi Awolowo University Teaching Hospital Complex (OAUTHC).  The predictive model was formulated using naïve Bayes’, decision trees and multi-layer perceptron algorithm – supervised machine learning algorithms.  The formulated model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) environment.  The results of the performance evaluation of the machine learning algorithms showed that the C4.5 decision trees and the multi-layer perceptron with an accuracy of 74.4% each outperformed the naïve Bayes’ algorithm.  In addition, the decision trees algorithm recognized variables relevant to predicting infertility and a rule that can be applied on patient risk factor records for infertility likelihood prediction was deduced from the tree structure.  This showed how effective machine learning algorithms can be used in predicting the likelihood of infertility in Nigerian women

Comparative Analysis of Prognostic Model for Risk Classification of Neonatal Jaundice using Machine Learning Algorithms

This study focused on the development of a prediction model using identified classification factors in order to classify the risk of jaundice in selected neonates. Historical dataset on the distribution of the classification of risk of jaundice among neonates was collected using questionnaires following the identification of associated classification factors of risk of jaundice from medical practitioners. The dataset containing information about the classification factors identified and collected from the neonates were used to formulate predictive model for the classification of risk of jaundice using 2 machine learning algorithm – Naïve Bayes’ classifier and the multi-layer perceptron.The predictive model development using the decision trees algorithm was formulated and simulated using the WEKA software.The predictive model developed using the multi-layer perceptron and Naïve Bayes’ classifier algorithms were compared in order to determine the algorithm with the best performance.The result shows that 10 variables were identified by the medical expert to be necessary in predicting jaundice in neonates for which a dataset containing information of 23 neonates alongside their respective jaundice diagnosis (Low, Moderate and High) was also provided with 22 attributes following the identification of the required variables.The 10-fold cross validation method was used to train the predictive model developed using the machine learning algorithms and the performance of the models evaluated The multi-layer perceptron algorithm proved to be an effective algorithm for predicting the diagnosis of jaundice in Nigerian neonate