Predicting Seminal Quality and its Dependence on Life Style Factors Through Ensemble Learning

Abstract

The awareness related to fertility is of great importance due to the change in lifestyle habits. Semen analysis is a reliable confirmatory test to check the fertility in men. The supervised machine learning models of base classifiers include Decision Tree, Logistic Regression and Naive Bayes classifiers in which logistic regression shows a promising accuracy of 88%. Comparing with the bagging ensemble method for the weakest classifier, the results show a leap in accuracy from 78.80% to 90.02%. The authors have also attempted to design a novel voting classifier which votes over the ensemble learners and creates a more complex model to give an accuracy of 89%. Apart from this, the authors have also analyzed the receiver operating characteristic (ROC) curve for Extra Tree classifier which shows a 66% of area under the curve (AUC). The validation procedure used is a 5 fold cross-validation. The authors have further analyzed the lifestyle habits responsible for contributing to this problem based on impurity-based feature selection and have obtained ‘Age' as the most crucial factor in declining seminal quality.

Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP

“…Most studies deal with an imbalanced dataset with missing model interpretation, which motivated our research. We employed the same techniques [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 19 ] and performance evaluation measures (ACC in %, SEN, SPEC, F1-Score, and AUC) to detect male fertility. Besides this, two more essential steps, data balancing and validation, are considered to build a robust and effective prediction model.…”

“…Of all, ADA performed best, with an accuracy of 95.1%. Dash and Ray [ 19 ] selected eight classifiers: soft voting, DT, NB, LR, DT, DT bagged, RF, and extra tree (ET). The maximum accuracy of 90.02% was achieved via ET.…”

Predicting the Causal Effect Relationship Between COPD and Cardio Vascular Diseases

“…Subsequently, it determines the average of the results from different decision trees to determine the class to which the object belongs to. It is an ensemble method [4,3] for classification as well as a regression that operates by constructing an assembly of decision trees at training time and finding out the class that is the most suitable for its outcome depending upon its predicted values.…”

Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE

“…In addition, SMOTE technique is used to overcome data imbalance issues. Dash and Ray [16] performed the comparative study using eight classifiers: soft voting, DT, NB, LR, DT bagged, RF, and ET. The maximum accuracy of 90.02% was achieved by ET.…”

“…In the literature mentioned above, authors have worked on male fertility detection by considering only improvement accuracy level and imbalanced dataset handling [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Unless the previously developed systems for predicting male fertility performed well, the articles in which system explainability issues are discussed have yet to be identified.…”