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Scientific Reports
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10.1038/s41598-024-71854-w
Article
Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques
Moulaei Khadijeh 15
Mahboubi Mohammad 2
Ghorbani Kalkhajeh Sasan 24
http://orcid.org/0000-0002-8882-5765
Kazemi-Arpanahi Hadi h.kazemi@abadanums.ac.ir

3
1 https://ror.org/042hptv04 grid.449129.3 0000 0004 0611 9408 Department of Health Information Technology, Faculty of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
2 https://ror.org/03w04rv71 grid.411746.1 0000 0004 4911 7066 Department of Public Health, Abadan University of Medical Sciences, Abadan, Iran
3 https://ror.org/03w04rv71 grid.411746.1 0000 0004 4911 7066 Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
4 Department of Community Medicine, School of Medicine, Abadan University of Medical Sciences, Abadan, Iran
5 https://ror.org/03w04rv71 grid.411746.1 0000 0004 4911 7066 Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran
6 9 2024
6 9 2024
2024
14 2081115 4 2024
1 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The declining fertility rate and increasing marriage age among girls pose challenges for policymakers, leading to issues such as population decline, higher social and economic costs, and reduced labor productivity. Using machine learning (ML) techniques to predict the desire to have children can offer a promising solution to address these challenges. Therefore, this study aimed to predict the childbearing tendency in women on the verge of marriage using ML techniques. Data from 252 participants (203 expressing a "desire to have children" and 49 indicating "reluctance to have children") in Abadan, and Khorramshahr cities (Khuzestan Province, Iran) was analyzed. Seven ML algorithms, including multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR), random forest (RF), J48 decision tree, Naive Bayes (NB), and K-nearest neighbors (KNN), were employed. The performance of these algorithms was assessed using metrics derived from the confusion matrix. The RF algorithm showed superior performance, with the highest sensitivity (99.5%), specificity (95.6%), and receiver operating characteristic curve (90.1%) values. Meanwhile, MLP emerged as the top-performing algorithm, showcasing the best overall performance in accuracy (77.75%) and precision (81.8%) compared to other algorithms. Factors such as age of marriage, place of residence, and strength of the family center with the birth of a child were the most effective predictors of a woman's desire to have children. Conversely, the number of daughters, the wife's ethnicity, and the spouse's ownership of assets such as cars and houses were among the least important factors in predicting this desire. ML algorithms exhibit excellent predictive capabilities for childbearing tendencies in women on the verge of marriage, highlighting their remarkable effectiveness. This capacity to offer accurate prognoses holds significant promise for advancing research in this field.

Keywords

Machine learning
Reproductive behavior
Fertility
Childbearing tendency
Marriage
Forecasting
Subject terms

Health policy
Public health
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

The population has a significant impact on the economic and social dynamics of each country. Politicians must competently manage fertility rates to achieve sustainable development1,2. Fertility is a crucial natural phenomenon with a decisive impact on population growth in any society, necessitating nations to invent and implement policies to strengthen it3,4. Fertility has become a major global concern. In recent years, most countries and regions have experienced an unprecedented decline in total fertility rate (TFR) that fell below the replacement level5–7. Iran has seen a drastic drop in fertility since 1986, with the number of children per woman decreasing from seven in 1990 to about 1.9 in 20078. According to a report from the Statistical Center of Iran based on the 2016 census, the population growth rate has decreased to less than 1%, leading to a shift in the age structure of the Iranian population from young to middle-aged and aging. This decline in Iran can be attributed to the impact of the eight years of imposed war and the specific policies of that era, at one point followed by the promotion of two children or less9.

Childbearing tendency refers to the desired number of children that families want, taking into account the number of their children ever born (CEB)10. It is important to note that this factor directly influences fertility behavior11. Making decisions about whether to have children is a significant event in a couple's life and is influenced by various demographics, economic, social, and individual factors12. Some sociologists and policymakers believe that the fertility rate is mainly affected by the preferences and desires of families regarding the number of children they want. However, the choice to have a child is seen as a private matter and is mainly given to couples, especially women13. A declining birth rate can result in an aging population, potentially straining healthcare systems and social welfare resources, as well as leading to labor force shortages and impacting economic growth14. Individuals who opt against parenthood may encounter societal pressures and stigmas, affecting their well-being and mental health15. This decision can also reshape family dynamics, influencing relationships and support structures as individuals age, and it can have an impact on pension and retirement systems, making it more challenging for the elderly population to receive necessary financial support16. Furthermore, a lower birth rate may impact the diversity and skill set of the future workforce and have implications for gender equality in societies with strong preferences against childbearing17.

Machine learning (ML) techniques can be used to predict childbearing tendencies in women, which can have both individual and societal benefits. Firstly, it provides women with valuable insights into their reproductive choices, enabling better family planning and life decision-making18,19. Moreover, these predictive models can assist healthcare providers in offering personalized counseling and medical care, leading to improved reproductive health outcomes20. At a societal level, this technology helps policymakers more efficiently allocate resources for family-related services and address demographic challenges21. Islam et al.19, investigated the prevalence of malnutrition among women in Bangladesh. They concluded that using ML technology to predict malnutrition in women could help healthcare providers and policymakers proactively implement necessary interventions. This proactive approach could be highly beneficial in preventing severe complications and reducing the burden on the healthcare system.

There is currently no study that uses ML techniques to predict the childbearing tendency in women on the verge of marriage. While many studies have looked into various aspects of fertility and family planning22–24, using ML techniques to predict women's childbearing tendency at the cusp of marriage remains uncharted territory. Our study aims to fill this gap by leveraging advanced ML algorithms and data analytics to gain insights into the childbearing tendencies of women approaching marriage. Therefore, we will compare different ML algorithms to select the best technique for predicting childbearing tendency in women on the verge of marriage.

Methods

Study design and setting

This was a retrospective and cross-sectional study conducted in 2023. The data for 252 women on the verge of marriage was collected from the marriage centers affiliated with Abadan University of Medical Sciences, Abadan City, Iran. The data was collected retrospectively and did not involve any interviews with the participants. All the data used in the research was anonymized to protect the participants' identities. The included cases were defined based on multiple variables that were categorized into demographic, socio-economic, and childbearing tendency sections. The main outcome variable was the childbearing tendency, which was categorized as "yes" if they desired to have one or more children, or "no" if they expressed no desire for children.

Pre-processing of the data

After collecting the data, we categorized and organized it based on its type. Records with missing values of 85% or more were excluded from the study. We also removed the variable representing participants' file numbers and added the participants' names to the database anonymously. Then, we carefully examined the data collection process for preprocessing, which involved managing missing values and ensuring data validity. Additionally, we computed the number of empty records for each factor influencing the prediction of childbearing tendencies in women on the verge of marriage. Blank records and data entered beyond the normal or feasible range were thoroughly reviewed and corrected if necessary.

Data balancing

The dataset we have selected has a significant imbalance in the distribution of outcome classes. Specifically, there is a large overrepresentation of samples in the "reluctance to have children" class, totaling 49 cases, while the "desire to have children" class contains a much smaller subset of only 203 cases. As a result, our trained models often produce biased results, leaning towards favoring the majority class, which increases the likelihood of new observations being classified into the majority category. To address this class imbalance issue in our study, we utilized the synthetic minority oversampling technique (SMOTE) method, effectively balancing the dataset25.

Feature selection

The process of feature selection entails comprehending datasets and selecting attributes that provide the necessary data for drawing the sought-after knowledge. This procedure also known as feature selection, involves identifying a subset of data from a large dataset26. In this study, we used the correlation-based features selection (CFS) technique to select features. This technique involves calculating correlations using various statistical measures and ranking or selecting attributes based on their correlation strength. Feature selection methods, such as the correlation attribute evaluator, aid in identifying relevant attributes that contribute the most to the predictive accuracy of an ML model. By selecting attributes that are highly correlated with the class attribute, this method aims to improve the efficiency and performance of the learning algorithm by focusing on the most informative features.

Machin learning algorithms

In our pursuit of constructing an early prediction algorithm for childbearing tendency in women on the verge of marriage, we harnessed the power of seven ML algorithms: Multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR), random forest (RF), J48 decision tree, Naive Bayes (NB), and k-nearest neighbors (KNN)25,27,28. We meticulously fine-tuned these models' hyperparameters through empirical experimentation, employing the cross-validation method on the training subset of our dataset. To gauge the efficacy of these ML algorithms, we adopted the widely-used holdout technique. The holdout technique is a common method used in ML and statistics for evaluating the performance of a predictive model29,30. It involves splitting a dataset into two or more subsets: one for training the model (70% training) and one for testing the model's performance (30% test). Subsequently, the ML algorithms were trained on the initial dataset split and evaluated on a separate portion, facilitating a robust assessment of their predictive capabilities25,27.

Cross-validation

To evaluate the classification models, we used a tenfold cross-validation process system to assess performance and generalize errors. We employed WEKA's EXPERIMENTER module to run all models ten times with repeated tenfold cross-validation. This allowed us to easily compare predictive performance based on various evaluation measures available in WEKA30,31. In the tenfold cross-validation process, the original dataset was randomly divided into ten sub-samples of roughly equal size. One of these sub-samples served as the validation dataset for testing the models, while the remaining nine sub-samples were used as training datasets. This cross-validation method was repeated ten times, with each of the ten sub-samples sequentially employed for validation. The validation results from these ten experimental models were combined to derive performance metrics, including sensitivity, specificity, accuracy, precision, and receiver operating characteristic (ROC) curve, obtained exclusively through testing32,33.

Additionally, the results of these performance metrics were calculated as an average of the ten runs. We favored stratified tenfold cross-validation for estimating accuracy due to its relatively low bias and variance compared to conventional split-instance methods. It's important to note that the tenfold cross-validation technique is widely preferred in the fields of ML, as it offers several advantages, such as reducing prediction error deviation, maximizing the use of data for both training and validation without introducing overfitting or overlaps between test and validation data, and safeguarding against biases introduced by arbitrary data splitting methods25,34.

Evaluating and validating the performance of the algorithms

The performance of the ML algorithms was rigorously assessed by utilizing confusion matrix performance metrics, as presented in Table 1. To comprehensively evaluate the predictive algorithms, a range of evaluation measures, including accuracy, specificity, precision, sensitivity, and the ROC curve criteria, were employed to gauge algorithm performance. The comparison of these diverse evaluation criteria allowed us to identify the optimal algorithm for predicting childbearing tendency in women on the verge of marriage, as detailed in Table 2.Table 1 Confusion matrix.

Output		Actual value	
Positive ( +)	Negative (-)	
Predicted value	Positive ( +)	TP	FN	
	Negative (-)	FP	TN	
* True positive (TP): when the actual value is Positive and the predicted is also Positive.

* False positive (FP): when the actual value is Negative and the prediction is also Negative.

* True negative (TN): When the actual is negative but the prediction is Positive. Also known as the Type 1 error.

* False negative (FN): When the actual is Positive but the prediction is Negative. Also known as the Type 2 error.

Table 2 The performance evaluation measures.

Performance criteria	Calculation	
Accuracy	TP+TNTP+FP+TN+FN	
Precision	TPTP+FP	
Sensitivity/ Recall	TPTP+FN	
Specificity	TNFP+TN	

Ethical considerations

This study was approved by the Ethical Committee Board, Abadan University of Medical Sciences (code: IR.ABADANUMS.REC.1402.129). To protect the privacy and confidentiality of the participants, we concealed the unique identifying information of all the participants in the process of data collection.

Results

Table 3 displays the demographic characteristics of the participants. Most participants lived in urban areas, and the majority were housewives. Additionally, most participants identified as ethnically Arab.Table 3 Demographic characteristics of the participants.

Variables	Categories	Number	Percent	
Resident place	City	189	75	
Rural	63	25	
Age	 ≤ 15 years	17	6.74	
16–19	57	22/61	
20–24	63	25	
25–29	55	21/82	
30–39	37	14/68	
40 ≤ 	23	9/12	
Ethnicity	Arab	193	77/58	
Kurd	11	4/36	
Lore	15	5/59	
Fars	25	9/92	
Other	8	3/17	
Occupational status	Employed	58	23.01	
Student	42	16.66	
Housewife	126	49/99	
Job search	26	10/31	
Educational level	Illiterate and primary	23	9/12	
Secondary	140	55/55	
University	75	29/76	
Master's degree or Doctor of Philosophy (Ph.D)	14	5.55	
The impact of government support in the decision to childbearing	Very low	78	30/95	
Low	107	44/46	
High	28	11/11	
Very high	39	15/47	
Income level	Without income	41	16/26	
Under 6 million	68	26/98	
Between 6–12 million	59	23/41	
Above 12 million	33	13/09	
Unknown	51	20/23	

Features selection

In total, the prediction of childbearing tendency in women on the verge of marriage using ML techniques was done based on 40 features. Factors such as "age at marriage," "place of residence," and "family support for childbearing" were found to be the most influential in predicting childbearing tendencies among women (Table 4).Table 4 Effective features used in the data set of predicting childbearing tendency in women on the verge of marriage.

Row	Feature (values)	Values of each effective feature	
1	Marriage age	0.246156	
2	Residence (town or village)	0.210719	
3	Strength of the family center with the birth of a child	0.209021	
4	Disruption of mother's fitness	0.207561	
5	Number of brothers	0.179559	
6	Feeling happy in large families	0.157595	
7	Women's educational and career progress is more important than having children	0.143447	
8	Government support for childbearing	0.132905	
9	Trying to get pregnant again if the sex of the child is not expected	0.119272	
10	Age gap between children	0.113298	
11	Mother's age	0.098173	
12	Did not like the father or mother	0.097043	
13	Being blamed for not having children	0.09656	
14	Blaming mothers for having more than two children	0.093309	
15	Spending income on things other than children	0.09147	
16	How long after marriage to have a child?	0.087201	
17	Number of boy children	0.087016	
18	Total monthly income of the family	0.082365	
19	Accompanying and helping children to their parents in old age	0.079756	
20	Coldness of life without children	0.075	
21	The wife's average monthly income	0.073623	
22	Strengthening the power of responsibility by having children	0.068942	
23	Loss of peace and comfort with having children	0.067256	
24	Employment status	0.054114	
25	Size of the area of the home	0.053407	
26	Duration of becoming a parent after marriage	0.046933	
27	Number of sisters	0.044728	
28	The high cost of raising a child is an obstacle to having children	0.04167	
29	Mother's average monthly income	0.039244	
30	Different views on having children	0.037026	
31	Gender	0.029195	
32	Delay in having children due to mistrust of spouse	0.028823	
33	Mother's ownership of car, house, and…	0.027409	
34	Level of education	0.02553	
35	Women's ethnicity	0.023759	
36	Gender of children	0.014064	
37	Daily activity level	0.007923	
38	Number of daughter children	0.007508	
39	Wife's Ethnicity	0.001142	
40	Spouse's ownership of cars, houses, etc	0.000385	

Performance evaluation results of selected algorithms

The RF algorithm outperformed other algorithms, demonstrating the highest sensitivity (99.5%), specificity (95.6%), and ROC (90.1%) values in our assessment. Furthermore, the MLP algorithm emerged as the top-performing algorithm, exhibiting superior overall performance with the highest accuracy (77.75%) and precision (81.8%) compared to other algorithms (Table 5). Figure 1 shows the performance evaluation results of the selected algorithms.Table 5 Performance evaluation of selected algorithms.

Algorithm	Performance of each algorithm	
Sensitivity (%)	Specificity (%)	Accuracy (%)	Precision (%)	ROC (%)	
RF	95.6	95.6	75.35	81.7	82.5	
LR	80.5	80.5	69.38	80.1	76.9	
J48 decision tree	83.9	83.9	66.45	77.5	74.7	
NB	83.9	83.9	82.2	79.6	76.5	
SVM	87.3	87.3	64.65	75.8	64.6	
MLP	87.8	87.8	77.75	81.8	81.2	
KNN	94.6	94.6	60.05	72.7	60.6	
Significant values are in [bold].

Fig. 1 Performance evaluation of selected algorithms.

Additionally, Fig. 2 displays ROC curves that compare ML algorithms for predicting childbearing tendencies in women on the verge of marriage.Fig. 2 ROC curves comparing ML algorithms for prediction of childbearing tendency in women on the verge of marriage.

Discussion

In this study, we evaluated the performance of several ML algorithms in predicting childbearing tendencies in women on the verge of marriage. The RF algorithm demonstrated the highest sensitivity, specificity, and ROC values, while the MLP algorithm exhibited the highest accuracy and precision. Therefore, both the RF and MLP algorithms are best suited for predicting the likelihood of childbearing among women on the verge of marriage. These algorithms have shown great effectiveness in various fields and offer unique advantages35–43. Although no specific research has focused on using ML for predicting childbearing tendencies, other studies on human reproductive behavior support our findings, indicating the superiority of RF and MLP in this context. Several studies44–47 have highlighted the feature selection capability of RF, which assigns important values to each feature, reducing the variable space. This feature selection step is a significant characteristic of the RF algorithm. Kebede et al.48 conducted a study on the predictors of unmet needs for family planning using advanced ML algorithms. They analyzed a weighted sample of 5,819 women and found that the RF model performed best, achieving an 85% accuracy rate and a 0.93 area under the curve (AUC). Meanwhile, Xiaoxia et al.49 used the China Migrants Dynamic Survey dataset to investigate the factors influencing fertility behaviors among the floating population. They utilized various models such as LR, multiple linear regression, artificial neural network (ANN), NB, and LR to make predictions. The results indicated that both the ANN and LR models had the best prediction performance. The study concluded that personal status, duration of settlement, scope of migration, economic conditions, and social services all influenced the reproductive behavior of the floating population. Furthermore, Kebede et al.50 trained eight ML algorithms to predict contraceptive discontinuation and found that RF was the most effective predictive model, achieving an accuracy of 68% and an ROC of 0.74 based on tenfold cross-validation. Similarly, Fayemiwo et al.51 utilized three ML algorithms, including KNN, DT, and RF to predict the survival of infants during childbirth. Their study also concluded that the RF algorithm outperformed other models. In a study conducted by Adem et al.52 in Ethiopia, eight different ML algorithms, namely Adaptive Boosting (AdaBoost), LR, RF, KNN, ANN, SVM, NB, and Extreme Gradient Boosting (XGBoost) were utilized. Using tenfold cross-validation and balanced training data, the RF model achieved the highest accuracy of 77.0% and an AUC of 85%, making it the most effective prediction model. These findings collectively support the potential of the RF algorithm in accurately forecasting childbearing tendencies, thus providing valuable insights for reproductive health interventions and policy-making.

Additionally, our study found that the MLP neural network model successfully predicted childbearing. It outperformed other algorithms, including RF, in terms of some evaluation criteria. The benefit of the MLP is its ability to generalize and extrapolate to various domains, which is particularly useful for dealing with large and diverse datasets53. Furthermore, the MLP has demonstrated high accuracy in multiple other fields54–56. Ameen et al.57, used an MLP model to predict women's childbearing preferences based on the desired number of children and age. The model achieved a 99.4% accuracy rate during training for those wanting four or fewer children, and 86.4% accuracy for those desiring five or more children. This study showcases the MLP's effectiveness in forecasting childbearing trends, employing a learning mechanism akin to human brain neural pathways. In another study, Baweja et al.58, aimed at improving predictive methods for the polycystic ovarian syndrome (PCOS) by comparing five algorithms: classification and regression tree (CART), SVM, NB, LR, and MLP. The results showed that the MLP algorithm outperformed the others, increasing baseline accuracy from 85 to 93%. The implications of these advancements are profound. By leveraging the efficiency and accuracy of these models, healthcare providers can offer more personalized and proactive care, potentially improving patient outcomes59. The versatility, accuracy, and cost-efficiency of the MLP neural network model make it an indispensable tool in the ongoing effort to harness the power of ML for the betterment of healthcare outcomes. Future research should continue to explore and refine these models, expanding their application to new areas and further integrating them into the healthcare landscape to maximize their impact.

In the end, it should be said that this study integrates an innovative approach that leverages the capabilities of ML to predict childbearing tendencies in women on the verge of marriage. While the use of standard ML algorithms is well-documented, our research stands out by meticulously identifying and validating the most influential socio-demographic and economic factors impacting childbearing. By deploying a comprehensive suite of seven ML algorithms, including advanced techniques like the MLP and RF, we not only achieved high accuracy but also uncovered critical predictors such as the age of marriage and family dynamics. Our findings provide actionable insights that can inform policy decisions aimed at addressing declining fertility rates and their associated socio-economic challenges. This nuanced application of ML underscores the study's significance, offering a valuable framework for future research and practical interventions in demographic studies.

Study limitations

The primary limitations of our research are associated with the dataset and its representativeness. While the study contributes valuable insights into predicting childbearing tendencies in women on the verge of marriage using ML algorithms, it is essential to acknowledge that the dataset was specific to Abadan, and Khorramshahr cities in Khuzestan Province, Iran. This geographic specificity may limit the broader applicability of our findings to a more diverse range of populations globally. Additionally, although our study included 252 participants, conducting further research with larger and more diverse samples could enhance the robustness and generalizability of the ML models for predicting childbearing tendencies. Moreover, while seven different ML algorithms were employed, it is essential to recognize that other algorithms not included in this study may offer different insights. The chosen algorithms were representative but not exhaustive.

Conclusions

Our research underscores the significant capabilities of ML algorithms, with a particular emphasis on RF and MLP, in accurately predicting childbearing tendencies among women on the verge of marriage. These algorithms exhibit outstanding performance across a spectrum of metrics, encompassing sensitivity, specificity, accuracy, precision, and ROC indices. Their exceptional effectiveness not only contributes to the precision of predicting childbearing tendencies within this demographic but also has the potential for enhancing outcomes and propelling advancements in research within this domain. Integrating ML in this context represents a significant step forward, and provides valuable tools for clinicians and researchers studying the childbearing tendencies of women on the verge of marriage. This technological advancement promises to refine decision-making processes and foster innovative research avenues, leading to a deeper comprehension of the factors influencing childbearing choices in this particular demographic.

Acknowledgements

We thank the research deputy of the Abadan University of Medical Sciences for financially supporting this project. (ABADANUMS.REC.1402.129).

Author contributions

KM, HKA, and MM: conceptualization; data curation; formal analysis; investigation; software; roles/writing—original draft. KM, HKA, and MM: conceptualization; formal analysis; investigation; roles/writing—original draft; funding acquisition; methodology; project administration; resources; supervision; writing—review and editing. HKA, KM, and SGHK: conceptualization; investigation; methodology; validation; writing—review and editing.

Funding

There was no funding for this research project.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Declarations

The design and performance of our study are described and justified in a research protocol. The protocol includes information regarding funding, sponsors (funded), institutional affiliations, potential conflicts of interest, incentives for subjects, and information regarding provisions for treating and/or compensating subjects who are harmed as a consequence of participation in the research study. This protocol is as follows:

Ethics approval and consent to participate

All experimental protocols were approved by the Abadan University of Medical Science ethical committee (ABADANUMS.REC.1402.129). All methods were carried out under relevant guidelines and regulations (Declaration of Helsinki). As the nature of the study is retrospective, the ethical committee of the Abadan University of Medical Sciences has waived informed consent for this study.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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