Internal validation and comparison of predictive models to determine success rate of infertility treatments: a retrospective study of 2485 cycles (original) (raw)

Machine Learning Approaches to predict Intra-Uterine Insemination Success Rate- Application of Artificial Intelligence in Infertility

2021

Introduction: Assisted Reproductive Technology (ART) has been widely utilized for infertility management. Despite its low success rate, Intra-Uterine Insemination (IUI) is one of the first alternatives and most important approaches regarding many cases of infertility treatment. Given the numerous influencing factors and limitations associated with time and resources, the development of a reliable model to predict the success rate of ART methods can significantly contribute to decision-making processes. Materials and methods: We reviewed the demographic, clinical, and laboratory data regarding 157 IUI treatment cycles among 124 women using their partner’s sperm from May2017 to June2019. Primary outcome measures were clinical pregnancy and live birth. Some prediction models were constructed and compared to the logistic regression analysis. Results: Woman’s mean age was 30.1 ± 5.2 years and the infertility had a female cause in 24.3% of the cases, male cause in 32.6% of cases, and comb...

Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization

PLOS ONE, 2022

Introduction Assisted reproductive technology has been proposed for women with infertility. Moreover, in vitro fertilization (IVF) cycles are increasing. Factors contributing to successful pregnancy have been widely explored. In this study, we used machine learning algorithms to construct prediction models for clinical pregnancies in IVF. Materials and methods A total of 24,730 patients entered IVF and intracytoplasmic sperm injection cycles with clinical pregnancy outcomes at Taipei Medical University Hospital. Data used included patient characteristics and treatment. We used machine learning methods to develop prediction models for clinical pregnancy and explored how each variable affects the outcome of interest using partial dependence plots. Results Experimental results showed that the random forest algorithm outperforms logistic regression in terms of areas under the receiver operating characteristics curve. The ovarian stimulation protocol is the most important factor affecting pregnancy outcomes. Long and ultralong protocols have shown positive effects on clinical pregnancy among all protocols. Furthermore, total frozen and transferred embryos are positive for a clinical pregnancy, but female age and duration of infertility have negative effects on clinical pregnancy.

IJERT-A Survey on the Machine Learning Techniques used in IVF Treatment to Improve the Success Rate

International Journal of Engineering Research and Technology (IJERT), 2019

https://www.ijert.org/A-Survey-on-the-Machine-Learning-Techniques-used-in-IVF-Treatment-to-Improve-the-Success-Rate https://www.ijert.org/research/A-Survey-on-the-Machine-Learning-Techniques-used-in-IVF-Treatment-to-Improve-the-Success-Rate-IJERTCONV7IS08080.pdf In vitro fertilization (IVF) is one of the widely used assisted reproductive technologies which help the couple suffering from infertility related issues to have a child. During IVF, an egg is removed from the woman's ovaries and fertilised with sperm in a laboratory. The fertilised egg, called an embryo, is then returned to the woman's womb to grow and develop. Since this treatment involves an application of several hormones and medicines to both female and male patients, it is very complicated and stressful process. Even after undergoing this costly treatment, there is no guarantee that the couple will get the positive result. There are many cases in which the IVF cycle fails and thereafter people will lose their hope of having a child. So, it is essential to have some method in which it is possible to predict the possibilities of having success in the treatment. The best possible management of the in-vitro fertilization treatment and patient advice is crucial for both patients and medical practitioners. The ultimate aim of infertile couple is the success of IVF treatment which depends on a number of influencing attributes. Without the automated tools, it is difficult for the doctors to assess any influencing trend of the attributes and factors which can lead successful pregnancy. There were many studies conducted in this area in which different machine learning classification techniques such as artificial neural networks (ANN), support vector machines (SVM), Decision trees, naive bayes, K-nearest neighbour (KNN), multi layer perceptrons(MLP) , Random Forest were used. Some of the work focuses on helping the doctors to understand the trends and patters to help them in suggesting the patient to go for IVF treatment or not. Some of them concentrate on helping the embryologist to choose the right embryo which will result in successful pregnancy. Some authors also worked towards optimizing the algorithm by selecting the correct number of features.

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

2018

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.

Using artificial intelligence to predict the intrauterine insemination success rate among infertile couples

Middle East Fertility Society Journal, 2021

To evaluate the use of artificial intelligence (AI) in predicting the success rate of intrauterine insemination (IUI) treatment among infertile couples and also to determine the importance of each of the parameters affecting IUI success. This study was a retrospective cohort study in which information from 380 infertile couples undergoing IUI treatment (190 cases resulting in positive pregnancy test and 190 cases of failed IUI) including underlying factors, female factors, sperm parameters at the beginning of the treatment cycle, and fertility results were collected from 2013 to 2019 and evaluated to determine the effectiveness of AI in predicting IUI success. We used the most important factors influencing the success of IUI as a neural network input. With the help of a three-layer neural network, the accuracy of the AI to predict the success rate of IUI was 71.92% and the sensitivity and specificity were 76.19% and 66.67%, respectively. The effect of each of the predictive factors ...

Ensemble Machine Learning Models for Evaluation of Sperm Quality with Respect to Success Rate of Clinical Pregnancy in IVF, ICSI, and IUI Methods

Objective: Evaluation of the effect of sperm quality on the success rate of clinical pregnancy and the possibility of infertility. The primary objective was to determine the success rate of clinical pregnancy (CPR). The secondary objective was to evaluate the clinical pregnancy rate (FHR). Method: This retrospective study evaluated 1929 couples who were treated with In Vitro Fertilization (IVF), in Intracytoplasmic Sperm Injection (ICSI), and Intrauterine Insemination (IUI) was conducted in two infertility centers; while data from donated eggs or sperm and a surrogate uterus along with data from infertile couples with a combination of male and female factors were excluded. In this study, five ensemble machine-learning models were utilized to predict the success rate of clinical pregnancy. Results:Among the proposed ensemble models, the Random Forest (RF) model achieved the highest mean accuracy and area under the curve (AUC) and outperformed all other models in three procedures. Our...

Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program

Fertility & Reproduction

Objective: Hidden knowledge could be discovered within a large practical data of in vitro fertilization (IVF) practice. In this study, Machine learning-based data mining techniques were utilized to construct a reliable prediction model for clinical pregnancy in IVF. Study Design: A retrospective cohort multicenter study involving 4.570 IVF cycles. All patients underwent fresh embryo transfer at either the cleavage or blastocyst stage between January 2015 and December 2019. The experiment focused on utilizing tree-based classifiers to generate and compare the most effective prediction model that could predict a clinical pregnancy through clinical data. Additionally, each classifier is optimized via a genetic algorithm technique, along with the selection of variables. Results: Both the decision tree and random forest showed similar performance that was much better than the gradient boost. The two superior classifiers achieved a balanced accuracy of roughly 0.62. Additionally, each pre...

Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods

Medical Decision Making, 2014

Background. Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies. Objective. To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred. Design. Retrospective cohort study. Data Source. Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively. Methods. For each class...

Comparison of Artificial Neural Networks and Logistic Regression Analysis in Pregnancy Prediction Using the In Vitro Fertilization Treatment

Studies in Logic, Grammar and Rhetoric, 2013

Infertility is recognized as a major problem of modern society. Assisted Reproductive Technology (ART) is the one of many available treatment options to cure infertility. However, the efficiency of the ART treatment is still inadequate. Therefore, the procedure’s quality is constantly improving and there is a need to determine statistical predictors as well as contributing factors to the successful treatment. There is a concern over the application of adequate statistical analysis to clinical data: should classic statistical methods be used or would it be more appropriate to apply advanced data mining technologies? By comparing two statistical models, Multivariable Logistic Regression analysis and Artificial Neural Network it has been demonstrated that Multivariable Logistic Regression analysis is more suitable for theoretical interest but the Artificial Neural Network method is more useful in clinical prediction.

Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them

BMC Medical Informatics and Decision Making

Background This study sought to provide machine learning-based classification models to predict the success of intrauterine insemination (IUI) therapy. Additionally, we sought to illustrate the effect of models fitting with balanced data vs original data with imbalanced data labels using two different types of resampling methods. Finally, we fit models with all features against optimized feature sets using various feature selection techniques. Methods The data for the cross-sectional study were collected from 546 infertile couples with IUI at the Fatemehzahra Infertility Research Center, Babol, North of Iran. Logistic regression (LR), support vector classification, random forest, Extreme Gradient Boosting (XGBoost) and, Stacking generalization (Stack) as the machine learning classifiers were used to predict IUI success by Python v3.7. We employed the Smote-Tomek (Stomek) and Smote-ENN (SENN) resampling methods to address the imbalance problem in the original dataset. Furthermore, to...