CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

Abstract Background Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features. Methods For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11,255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples' demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcome. Results Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).

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CNFE-SE: A novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features

10.21203/rs.3.rs-24259/v3 ◽

2020 ◽

Author(s):

Sima Ranjbari ◽

Toktam Khatibi ◽

Ahmad Vosough Taghi Dizaj ◽

Hesamoddin Sajadi ◽

Mehdi Totonchi ◽

...

Keyword(s):

Complex Networks ◽

Complex Network ◽

Semen Quality ◽

Intrauterine Insemination ◽

Machine Learning Algorithms ◽

Semen Parameters ◽

Feature Engineering ◽

Operating Characteristics ◽

Scoring Method ◽

Novel Approach

Abstract Background: Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features. Methods: For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples' demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcome Results: Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions: The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).

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CNFE-SE: A novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features

10.21203/rs.3.rs-24259/v2 ◽

2020 ◽

Author(s):

Sima Ranjbari ◽

Toktam Khatibi ◽

Ahmad Vosough Taghi Dizaj ◽

Hesamoddin Sajadi ◽

Mehdi Totonchi ◽

...

Keyword(s):

Complex Networks ◽

Complex Network ◽

Semen Quality ◽

Intrauterine Insemination ◽

Machine Learning Algorithms ◽

Semen Parameters ◽

Feature Engineering ◽

Operating Characteristics ◽

Scoring Method ◽

Novel Approach

Abstract Background: Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features. Methods: For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcomeResults: Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions: The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).

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CNFE-SE: A novel hybrid approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features

10.21203/rs.3.rs-24259/v1 ◽

2020 ◽

Author(s):

Sima Ranjbari ◽

Toktam Khatibi ◽

Ahmad Vosough Taghi Dizaj ◽

Hesamoddin Sajadi ◽

Mehdi Totonchi ◽

...

Keyword(s):

Complex Networks ◽

Complex Network ◽

Treatment Cycle ◽

Intrauterine Insemination ◽

Hybrid Approach ◽

Machine Learning Algorithms ◽

Semen Parameters ◽

Feature Engineering ◽

Scoring Method ◽

Original Feature

Abstract Background: Intrauterine Insemination (IUI) outcome prediction is a challenging issue with which the assisted reproductive technology (ART) practitioners are dealing. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. The large number of studies that have been focused on predicting the IVF and ICSI outcome by machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to develop an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking of the most significant features, based on patients' cycle’s characteristics under IUI treatment.Methods: For this purpose, a novel hybrid approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute for 8,360 couples who underwent 11,255 IUI cycles were included. Results: Experimental results show that the proposed method outperforms the compared methods with AUC of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions: The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female BMI.

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Time to improvement in semen parameters after microsurgical varicocelectomy in men with severe oligospermia

Canadian Urological Association Journal ◽

10.5489/cuaj.5408 ◽

2018 ◽

Vol 13 (3) ◽

Cited By ~ 4

Author(s):

Thomas A. Masterson ◽

Aubrey B. Greer ◽

Ranjith Ramasamy

Keyword(s):

Semen Quality ◽

Intrauterine Insemination ◽

Sperm Count ◽

Semen Analysis ◽

Semen Parameters ◽

Vitro Fertilization ◽

Minimal Improvement ◽

Quality Upgrading ◽

Total Motile Sperm Count

Introduction: We aimed to determine the time and predictive factors of semen quality improvement in men with severe oligospermia after microsurgical varicocelectomy. Methods: Men with total motile sperm count (TMSC) <5 million on two semen analyses were identified from May 2015 to August 2017. Postoperative semen analysis was collected at 3–6 months and >6 months. We evaluated preoperative factors for successful semen quality upgrading based on assisted reproductive technology (ART) eligibility: in vitro fertilization [IVF] (<5 million), intrauterine insemination (IUI) (5–9 million), and natural pregnancy (>9 million). We compared men with TMSC <5 million to those with TMSC 5–9 million. Data are reported as means and standard error of the mean (SEM). Pregnancy data was collected by phone interview at >6 months postoperatively. Results: A total of 33 men were included. TMSC improved from 1.5±0.2 to 7.3±1.8 million at 3–6 months (p<0.05) and 12.2±3.6 million at >6 months (p<0.05). There was no statistical difference in TMSC between 3–6 months and >6 months. Sixteen (48.5%) men upgraded semen quality into the range of natural pregnancy. Preoperative TMSC from 2–5 million was predictive of upgrading semen quality. Twenty-four couples were contacted by phone; 20 were attempting pregnancy in the postoperative period and five (25%) of them had achieved natural pregnancy. Conclusions: Men with TMSC <5 million can expect the largest improvement in TMSC from 3–6 months postoperatively with minimal improvement thereafter. Preoperative TMSC >2 million was most predictive of semen quality upgrading.

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Information on evolutionary age in redundancy of complex network

Modern Physics Letters B ◽

10.1142/s0217984919503317 ◽

2019 ◽

Vol 33 (27) ◽

pp. 1950331

Author(s):

Shiguo Deng ◽

Henggang Ren ◽

Tongfeng Weng ◽

Changgui Gu ◽

Huijie Yang

Keyword(s):

Principal Component Analysis ◽

Complex Networks ◽

Metabolic Network ◽

Complex Network ◽

Cellular Networks ◽

Topological Structure ◽

Quantitative Measure ◽

Principal Component ◽

Evolutionary Information ◽

Local Patterns

Evolutionary processes of many complex networks in reality are dominated by duplication and divergence. This mechanism leads to redundant structures, i.e. some nodes share most of their neighbors and some local patterns are similar, called redundancy of network. An interesting reverse problem is to discover evolutionary information from the present topological structure. We propose a quantitative measure of redundancy of network from the perspective of principal component analysis. The redundancy of a community in the empirical human metabolic network is negatively and closely related with its evolutionary age, which is consistent with that for the communities in the modeling protein–protein network. This behavior can be used to find the evolutionary difference stored in cellular networks.

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Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210458 ◽

2021 ◽

pp. 1-17

Author(s):

Ahmed Al-Tarawneh ◽

Ja’afer Al-Saraireh

Keyword(s):

Machine Learning ◽

Complex Networks ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbor ◽

Efficient Detection ◽

Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

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Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

Future Internet ◽

10.3390/fi13030063 ◽

2021 ◽

Vol 13 (3) ◽

pp. 63

Author(s):

Maghsoud Morshedi ◽

Josef Noll

Keyword(s):

Machine Learning ◽

Network Performance ◽

Service Providers ◽

Video Conferencing ◽

Machine Learning Algorithms ◽

Performance Parameters ◽

High Definition ◽

Internet Service ◽

Novel Approach ◽

5 Ghz

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

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Topology Identification of Complex Network via Chaotic Ant Swarm Algorithm

Mathematical Problems in Engineering ◽

10.1155/2013/401983 ◽

2013 ◽

Vol 2013 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

Haipeng Peng ◽

Lixiang Li ◽

Jürgen Kurths ◽

Shudong Li ◽

Yixian Yang

Keyword(s):

Complex Networks ◽

Numerical Simulations ◽

Complex Network ◽

Parameter Optimization ◽

Ant Algorithm ◽

Structure Parameters ◽

Topology Identification ◽

Unknown Structure ◽

Swarm Algorithm ◽

Scientific Fields

Nowadays, the topology of complex networks is essential in various fields as engineering, biology, physics, and other scientific fields. We know in some general cases that there may be some unknown structure parameters in a complex network. In order to identify those unknown structure parameters, a topology identification method is proposed based on a chaotic ant swarm algorithm in this paper. The problem of topology identification is converted into that of parameter optimization which can be solved by a chaotic ant algorithm. The proposed method enables us to identify the topology of the synchronization network effectively. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed method.

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