Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment

Mirroshandel, Seyed Abolghasem; Ghasemian, Fatemeh; Monji-Azad, Sara

doi:10.1016/j.cmpb.2016.09.013

Cited by 22 publications

(24 citation statements)

References 25 publications

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“…The NN algorithm has been used in two studies 46,56 as single technique, and in other studies, it has been used along with other algorit hms. 6,25,37,39,[57][58][59][60] In accordance with our results, the NN algorithm in comparison with other algorithms has been selected as a more suitable method in ART outcome prediction. 37,39,59 Another superior algorithm in our study was RF.…”

Section: Discussionsupporting

confidence: 64%

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Computational prediction of implantation outcome after embryo transfer

Raef

Maleki

Ferdousi

2019

Health Informatics J

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The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle.

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Section: Discussionsupporting

confidence: 64%

“…37,39,59 Another superior algorithm in our study was RF. In support of this result, in three studies, 6,40,60 among the five studies using the RF algorithm along with others, the RF was identified as a better method. Also, in previous studies, 18,39 the RF algorithm has achieved performance close to that of the superior algorithm.…”

Section: Discussionmentioning

confidence: 65%

Computational prediction of implantation outcome after embryo transfer

Raef

Maleki

Ferdousi

2019

Health Informatics J

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“…We simulated a sequence of logit AUROCs to identify equivalent differences in AUROCs to approximate a difference of the logit value in the random effects model (1.22, 95% CI −0.03 to 2.48). AUROC differences of 0.206 and 0.026 were equivalent to a difference in the logit AUROC of 0.91, compared with those aggregated from LR models for the random forest models of Blank et al [158] and Mirroshandel et al [134], respectively. Using τ 2 , one can calculate the 95% prediction interval (PI) of the logit AUROC difference, as previously described [173].…”

Section: Comparison Of the Predictive Performancementioning

confidence: 99%

“…A random effects model developed for comparison of random forests and LR to predict ongoing pregnancy had the highest absolute value of heterogeneity (τ 2 =2.86). In this random effects model, random forests were applied to develop predictions in 2 studies that reported AUROCs of 0.740 (95% CI 0.710-0.770) [158] and 0.9820 [134]. We simulated a sequence of logit AUROCs to identify equivalent differences in AUROCs to approximate a difference of the logit value in the random effects model (1.22, 95% CI −0.03 to 2.48).…”

Section: Comparison Of the Predictive Performancementioning

confidence: 99%

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

et al. 2020

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Background Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. Objective This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians’ decision making. Methods Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians’ decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2. Results Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07). Conclusions Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106

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“…• Pregnancy Prediction Applying ML models to predict the success of IVF results of infertile patients when they undergo IVF treatment [27,28]. • Embryo Evaluation Applying the ML models to help the embryologists selecting a good quality embryo to transfer [29], which will increase the chance of pregnancy and decrease the risk of multiple pregnancies. The variability of applying AI and ML on different datasets from 16 follicles to 11,898 human embryos is fully described in the paper [30].…”

Section: B Machine Learningmentioning

confidence: 99%