Knowledge Discovery on In Vitro Fertilization Clinical Data Using Particle Swarm Optimization

Chen, Chih-Chuan; Hsu, Chao Chin; Cheng, Yi-Chung; Li, Sheng-Tun

doi:10.1109/bibe.2009.36

Cited by 5 publications

(3 citation statements)

References 9 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%

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%

Computational prediction of implantation outcome after embryo transfer

Raef

Maleki

Ferdousi

2019

Health Informatics J

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“…Chen and coauthors [12], proposed two phase Particle Swam Optimization (PSO) approach to predict IVF outcome using 654 IVF cycle data with 10 attributes. They also obtain an optimal rule set from the model by encoding decision rules into a particle position.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Model for Assisted Reproductive Technology Outcome Prediction

Kothandaraman

Suruliandi

Raja

2021

Braz. arch. biol. technol.

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Infertility is becoming a growing issue in almost all countries. Assisted Reproductive Technologies (ART) are recent development in treating infertility that give hope to the infertile couples. However, the pregnancy rates achieved with the aid of ART is considerably low, as success in ART is not only based on the treatment but also on many other controllable and uncontrollable biological, social, and environmental features. High expenditures and painful process of ART cycles are the two major barriers for opting for ART. Moreover, ART treatments are not covered by any health insurance schemes. Computational prediction models could be used to improve the success rate by predicting the treatment outcome, before the start of an ART cycle. This may suggest the couples and the doctors to decide on the next course of action i.e. either to opt for ART or opt for correcting determinants or quit the ART. With the intension to improve the success rate of ART by providing decision support system to the physicians as well to the patients before entering into the treatment this research work proposes a dynamic model for ART outcome prediction using Machine Learning (ML) techniques. The proposed dynamic model is partially implemented with the help of an ensemble of heterogeneous incremental classifier and its performance is compared with state-of-art classifiers such as Naïve Bayes (NB), Random Forest (RF), K-star etc.,using ART dataset. Performance of the model is evaluated with various metrics such as accuracy, Precision Recall Curve (PRC), Receiver Operating Characteristic (ROC), F-Measure etc., However, ROC cure area is taken as the chief metric. Evaluation results shows that the model achieves the performance with the ROC area value of 94.1 %. HIGHLIGHTS Proposed a dynamic model for ART outcome prediction  Partial implementation of the model with the help of machine learning incremental classifier  Performance evaluation of the model with the state-of-art methods.

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“…eproductive system disorders are known as a disease [1]. Infertility is a negative concept in ancient myths and civilizations [2] and one of the most important social problems creating structural effects in modern society [3]. According to the World Health Organization (WHO), in clinical terms, infertility is reproductive system disease, in which couples do not succeed in childbirth (clinical fertility) after twelve months or more of regular sex without using preventive methods [4].…”

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confidence: 99%

Use of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method

et al. 2019

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Background: Intracytoplasmic sperm injection (ICSI) or microinjection is one of the most commonly used assisted reproductive technologies (ART) in the treatment of patients with infertility problems. At each stage of this treatment cycle, many dependent and independent variables may affect the results, according to which, estimating the accuracy of fertility rate for physicians will be difficult.Objective: This study aims to evaluate the efficiency of artificial neural networks (ANN) and principal component analysis (PCA) to predict results of infertility treatment in the ICSI method. Material and Methods:In the present research that is an analytical study, multilayer perceptron (MLP) artificial neural networks were designed and evaluated to predict results of infertility treatment using the ICSI method. In addition, the PCA method was used before the process of training the neural network for extracting information from data and improving the efficiency of generated models. The network has 11 to 17 inputs and 2 outputs. Results:The area under ROC curve (AUC) values were derived from modeling the results of the ICSI technique for the test data and the total data. The AUC for total data vary from 0.7670 to 0.9796 for two neurons, 0.9394 to 0.9990 for three neurons and 0.9540 to 0.9906 for four neurons in hidden layers. Conclusion:The proposed MLP neural network can model the specialist performance in predicting treatment results with a high degree of accuracy and reliability.

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Knowledge Discovery on In Vitro Fertilization Clinical Data Using Particle Swarm Optimization

Cited by 5 publications

References 9 publications

Computational prediction of implantation outcome after embryo transfer

Computational prediction of implantation outcome after embryo transfer

Dynamic Model for Assisted Reproductive Technology Outcome Prediction

Use of Artificial Neural Networks and PCA to Predict Results of Infertility Treatment in the ICSI Method

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