Prediction of recurrent spontaneous abortion using evolutionary machine learning with joint self-adaptive sime mould algorithm

Shi, Beibei; Chen, Jingjing; Chen, Haiying; Lin, Wenjing; Yang, Jie; Chen, Yi; Wu, Chengwen; Huang, Zhiqiong

doi:10.1016/j.compbiomed.2022.105885

Cited by 26 publications

(6 citation statements)

References 55 publications

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“…However, there was considerable variation in the performance of the prediction models. On the other hand, Shi et al, 2022 successfully employed adaptive simulation modelling algorithms to utilize clinical data from patients with recurrent spontaneous abortion (RSA), vitamin D levels, and thyroid function to explore optimal parameters and sub-features during support vector machine (SVM) evolution. However, the study had a relatively small sample size ( n = 136).…”

Section: Discussionmentioning

confidence: 99%

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities

Wu,

Yu,

et al. 2024

Front. Pharmacol.

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Introduction: It is known that patients with immune-abnormal co-pregnancies are at a higher risk of adverse pregnancy outcomes. Traditional pregnancy risk management systems have poor prediction abilities for adverse pregnancy outcomes in such patients, with many limitations in clinical application. In this study, we will use machine learning to screen high-risk factors for miscarriage and develop a miscarriage risk prediction model for patients with immune-abnormal pregnancies. This model aims to provide an adjunctive tool for the clinical identification of patients at high risk of miscarriage and to allow for active intervention to reduce adverse pregnancy outcomes.Methods: Patients with immune-abnormal pregnancies attending Sichuan Provincial People’s Hospital were collected through electronic medical records (EMR). The data were divided into a training set and a test set in an 8:2 ratio. Comparisons were made to evaluate the performance of traditional pregnancy risk assessment tools for clinical applications. This analysis involved assessing the cost-benefit of clinical treatment, evaluating the model's performance, and determining its economic value. Data sampling methods, feature screening, and machine learning algorithms were utilized to develop predictive models. These models were internally validated using 10-fold cross-validation for the training set and externally validated using bootstrapping for the test set. Model performance was assessed by the area under the characteristic curve (AUC). Based on the best parameters, a predictive model for miscarriage risk was developed, and the SHapley additive expansion (SHAP) method was used to assess the best model feature contribution.Results: A total of 565 patients were included in this study on machine learning-based models for predicting the risk of miscarriage in patients with immune-abnormal pregnancies. Twenty-eight risk warning models were developed, and the predictive model constructed using XGBoost demonstrated the best performance with an AUC of 0.9209. The SHAP analysis of the best model highlighted the total number of medications, as well as the use of aspirin and low molecular weight heparin, as significant influencing factors. The implementation of the pregnancy risk scoring rules resulted in accuracy, precision, and F1 scores of 0.3009, 0.1663, and 0.2852, respectively. The economic evaluation showed a saving of ¥7,485,865.7 due to the model.Conclusion: The predictive model developed in this study performed well in estimating the risk of miscarriage in patients with immune-abnormal pregnancies. The findings of the model interpretation identified the total number of medications and the use of other medications during pregnancy as key factors in the early warning model for miscarriage risk. This provides an important basis for early risk assessment and intervention in immune-abnormal pregnancies. The predictive model developed in this study demonstrated better risk prediction performance than the Pregnancy Risk Management System (PRMS) and also demonstrated economic value. Therefore, miscarriage risk prediction in patients with immune-abnormal pregnancies may be the most cost-effective management method.

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Section: Discussionmentioning

confidence: 99%

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities

Wu,

Yu,

et al. 2024

Front. Pharmacol.

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“…JASMA-SVM, also known as joint self-adaptive SMA and standard kernel learning support vector machine (SVM) with maximum-margin hyperplane theory, is an acronym for these two algorithms. This combination creates a framework that is detailed [ 69 ]. A supplementary management approach (MSMA) that considers several populations has been suggested.…”

Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

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“…Excellent results were obtained by [70] in their study of the Recurrent Spontaneous Abortion (RSA) prediction. The base algorithm was a support vector machines, and the optimization was done through the feature selection of the RSA.…”

Section: Evolutionary Computing In Medicinementioning

confidence: 97%

Towards the Automatic Discovery of Rules Based on ECG Data for the Support of Personalized Medical Decisions

Zouri

2024

Preprint

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With the increasing amount of available medical data and particularity bio-signal data, there is a need to develop computerized techniques to support medical staff in providing effective and efficient treatment options. Personalized medical treatment requires the physician to provide treatment based on characteristics specific to the individual patient. This process is typically driven by subject matter expertise and established medical standards. The electrocardiogram is the most common non-invasive method for monitoring the health condition of the heart. In order to support the evidence-based decision-making process, available data can be examined to identify patterns that are relevant to the characteristics of the individual patient. For this process to be beneficial, the knowledge representation and discovery approach need to be easily understood and interpreted by humans as well as machine-readable. Also, the developed model must be flexible to accommodate the evolution of available medical data over time. In this dissertation, we proposed an approach for the development of a decision support system based on ontology and learning classifier systems. The use of ontology supports the integration of data from different sources and various formats, and it provides a mechanism for knowledge enrichment based on available medical standards and the addition of newly available data. Learner classifier systems can support the automatic discovery of rules that can be used to investigate the factors impacting the medical condition of the patient and conduct investigations in the form of what-if scenarios to identify the underlying conditions that are affecting the health of the heart. The approach we propose intends to allow physicians and researchers to use the inference between the attributes of the patient data to create custom what-if scenarios and determine otherwise hidden relationships between the data.

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Prediction of recurrent spontaneous abortion using evolutionary machine learning with joint self-adaptive sime mould algorithm

Cited by 26 publications

References 55 publications

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Towards the Automatic Discovery of Rules Based on ECG Data for the Support of Personalized Medical Decisions

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