Explainable machine learning algorithm to identify predictors of intention to use family planning among reproductive-age women in Ethiopia: Evidence from the performance monitoring and accountability (PMA) survey 2021 dataset

doi:10.21203/rs.3.rs-3848375/v1

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Research Article

Explainable machine learning algorithm to identify predictors of intention to use family planning among reproductive-age women in Ethiopia: Evidence from the performance monitoring and accountability (PMA) survey 2021 dataset

https://doi.org/10.21203/rs.3.rs-3848375/v1

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Introduction:

Approximately 225 million people in developing nations wish to delay or cease childbearing, but do not use any form of contraception. In the least developed countries, contraceptive usage was significantly lower, at 40%, and was particularly low in Africa at 33%. It is widely believed that intentions are a strong predictor of behavior, and many interventions that aim to change behavior including that targeting family planning use rely on evaluating program effectiveness through analyzing behavioral intentions. Understanding a woman's intention to use contraceptive methods is crucial in predicting and promoting the use of such methods. Therefore, this study aims to assess the determinants of intention to use family planning among reproductive age women in Ethiopia using explainable machine learning algorithm

Method

Secondary data from the cross-sectional household and female survey conducted by PMA Ethiopia in 2021 were used in the study. Using Python 3.10 version software, eight machine learning classifiers were used to predict and identify significant determinants of intention to use family planning on a weighted sample of 5993 women. Performance metrics were used to evaluate the classifiers. To smooth the data for additional analysis, data preparation techniques such as feature engineering, data splitting, handling missing values, addressing imbalanced categories, and outlier removal were used. Lastly, the greatest predictors of intention to utilize family planning were found using Shapley Additive exPlanations (SHAP) analysis, which further clarified the predictors' impact on the model's results.

Result

Using tenfold cross-validation and balanced training data, Random Forest revealed a performance of 77.0% accuracy and 85% areas under the curve, making it the most effective prediction model. The age at which family planning was first used, a partner or husband older than 40, being single, being Muslim, being pregnant, having previously been pregnant, needing to have more children, having a son or daughter relationship to the head of the household, and unmet needs for spacing and limiting were the top predictors of intention to use family planning, according to the SHAP analysis based on the random forest model. The research findings indicate that a range of personal and cultural factors may be taken into account when enacting health policies to enhance family planning intentions in Ethiopia. Therefore it’s highly recommended that the intention of family planning use and initiation of family planning provision should become a standard of service delivery to achieve the 2030 SDGs.

Machine learning algorithm

Intention to use family planning

Predictors

Ethiopia

In the next three decades, the population of the world is expected to increase by almost 40% (1). Over half of the additional 3 billion people will be in developing regions like sub-Saharan Africa and Asia, where the majority of the population growth is anticipated (2). Out of the 1.9 billion women of reproductive age in the globe, 1.1 billion need family planning services, and 190 million women wish to prevent getting pregnant but do not use any form of contraception(3, 4).

Overall, there is a considerable variation in terms of contraceptive prevalence rate among different methods, ranging from 4.6% in South Sudan to 72.1% in Canada (3). In 2020, approximately 225 million people in developing nations wish to delay or cease childbearing, but do not use any form of contraception (5). In the least developed countries, contraceptive usage was significantly lower, at 40%, and was particularly low in Africa at 33% (6). The utilization of family planning is a significant predictor of preventing unwanted pregnancies, reducing maternal and child mortality, and enhancing the wellbeing of women and their families (7). Moreover, it is critical to achieving the 2030 Sustainable Development Goals (SDGs) (8).

Globally, the average total fertility rate varies from 1.7 children per woman in the most developed countries to 4.6 in the least developed countries (9) Within Sub-Saharan Africa (SSA), total fertility rates range from 2.9 in Botswana to 7.2 in Niger (10, 11). The high unmet need for contraception across all age groups and marital statuses accounts for both the rapid population growth and the steady increase in fertility (10).

With an estimated 120 million people and an average annual growth rate of 2.6%, Ethiopia is the second most populous country in Sub-Saharan Africa (12). By 2050, Ethiopia's population is expected to reach 166 million, making it the tenth most populous nation in the world (9). Having a high overall fertility rate, high rates of maternal and infant mortality, and low rates of contraceptive use, Ethiopia is the second most populous country in Sub-Saharan Africa (12).

Total fertility rate in Ethiopia is 5.3 children per woman (9). As a result, Ethiopia is now among the nations with the highest global fertility rates. Low family planning utilization is a major contributing factor to the high levels of fertility, particularly in less developed nations like Ethiopia (13). The proportion of married women in Ethiopia who use contraception ranges from 5% in the Somalia region to 53% in Addis Ababa and the Amhara region. The overall modern contraceptive prevalence rate in the country is 40% (4).

Intention to use and use of family planning has been proven to be an effective method of controlling family size and reducing unintended pregnancies(14). It is widely believed that intentions are a strong predictor of behavior, and many interventions that aim to change behavior including those targeting contraceptive use rely on evaluating program effectiveness through analyzing behavioral intentions (15). Understanding a woman's intention to use contraceptive methods is crucial in predicting and promoting the use of such methods (16). By doing so, we can improve the health of not just women but also children, families, and even societies (17).

Numerous studies conducted on the use and intention to use family planning among reproductive age women have revealed factors such as switching between different forms of contraception (18), poor support from husband(19), maternal age (18), maternal education (4, 20), positive attitude to contraceptive use, occupation, knowledge of contraceptives (19, 21), discussion on family planning with husband(18), myths and misconceptions regarding contraception(20), time of birth interval(4), joint fertility decision(22), and desire for live children(19) among the variables influencing women of reproductive age's intention to use family planning. Similarly, the intention of family planning use among reproductive age women was significantly associated with, number of live children, and counseling during antenatal care, husbands' approval of family planning use, and having good knowledge of postpartum family planning(23, 24).

Traditional regression models were previously used in Ethiopia to demonstrate the effects of socioeconomic, demographic, behavioral, maternal, and service-related characteristics on the intention of reproductive-age women to use family planning; however, the validity of these results declined with an increase in the number of variables and potential correlations (25–29). The multidisciplinary relationships between variables and numerous factors are typically problematic for these traditional models (30, 31). Hence, in contrast to those traditional models, machine learning (ML) provides an effective way to find relevant characteristics linked to specific health outcomes for conducting public health research (30, 32, 33). Therefore, the purpose of this study is to evaluate the factors influencing Ethiopian women of reproductive age's intention to use family planning.

This study seeks to determine and uncover consistent variables as well as new factors that influence the intention to utilize family planning using data from Ethiopian women of reproductive age in the PMA Survey 2021 dataset. The Ethiopian Ministry of Health and other health partners will be able to enhance the intention of reproductive-age women in Ethiopia to utilize family planning by concentrating on the most consistent and influential variables that will be designated as priority areas for intervention.

Study design

A machine learning (ML) technique was applied using secondary data from the PMA Ethiopia 2021 cross-sectional household and female Survey. PMA-Ethiopia is a five-year (2019–2023) project in collaboration with Addis Ababa University, Johns Hopkins University, and the Federal Ministry of Health. It consists of three distinct study activities: yearly cross-sectional surveys of women aged 15–49; longitudinal surveys of women who are pregnant or have given birth; and yearly service delivery point surveys of health facilities (34).

Source and study population

All 15–49 aged women in Ethiopia.

Sample size

The sample size in the present study was weighted for taking non-response and variations in the probability of selection into consideration. Further, only women who were of reproductive age at the time of the survey were included in the sample. Therefore, in a weighted sample, only 5993 women of reproductive age were eligible to be included in the analysis.

Study variables

Dependent variable

The dependent variable was the intention to use family planning, which was divided into two groups: "intended to use" and "not intended to use."

Predictor variables

Sociodemograpic and economic factors including residence, women's age, region, and religion, size of family, education level, financial status, and media access are predictive of the intention to use family planning. The following reproductive health and family planning service characteristics were also included as predictors of intention to use family planning: partner or husband's feelings toward family planning, knowledge of all available contraceptive methods, age at first sex, having ever been pregnant, having ever used family planning (FP) methods, having ever been delivered in a health facility (HF), and partner being told not to use FP.

Data processing and analysis

In order to predict the intention to use family planning, this study employed the general framework found in previous research, which is based on Yufeng Guo's 7 Steps of Machine Learning. The following seven steps in supervised machine learning are outlined in the framework: data preparation, model training, model evaluation, parameter tuning, model selection, data collection, and prediction (35). Machine learning (ML) algorithms were implemented in Python 3.10.2 using Jupyter Notebook through Scikit-learn, and XGBoost packages.

Data source/collection

The dataset for this study is available on the PMA Survey website and can be obtained upon a formal request. A weighted sample of 5993 reproductive-age women is included in the study.

Data preparation/pre-processing

This study employed a variety of data preparation techniques, including feature engineering, data splitting, and data cleaning. Missing values were imputed using the R package 'CALIBERrfimpute' using Multivariate Imputation by Chained Equations (MICE) and random forest and the training data was balanced using the Synthetic Minority Oversampling Technique (SMOTE)(36). SMOTE creates synthetic observations by interpolating between minority classes samples in the feature space. The Kolmogorov-Smirnov test indicates that the synthetic and existing observations differ significantly from each other (KS test statistic of 0.8214, p-value = < 0.001). Using Pandas get-dummies, One-Hot-Encoding techniques were applied to encode categorical data into dummy variables, and the relationship between the predictors and the outcome variable was evaluated using the SHAP feature importance method. A tenfold cross-validation technique was used to train the models(36).

Model selection and training

We used eight different machine learning algorithms namely AdaBoost, logistic regression (LR), random forest (RF), K-nearest neighbor (KNN), artificial neural network, support vector machine (SVM), Naïve Bayes, and extreme gradient boosting (XGBoost) to predict intention to use family planning among reproductive age women and then determined which model was best suited for the problem(36). Tenfold cross-validation was used to compare the performance of the chosen classifiers, and the best predictive model was chosen after comparison, adjusted with its hyper-parameters, and trained using balanced training data.

Model evaluation and Hyper-parameter tuning

Following training, the model was evaluated using the area under the receiver operating characteristic curve (AUC) score and classification accuracy. Moreover, ML model performance was shown using the receiver operating characteristic (ROC) curve. The Optuna framework was used to adjust the best model's hyper-parameters. It uses the Bayesian framework to better understand the probability of the optimal values and prevents needless computation for the combination of non-performing parameters in the search(36).

Prediction

Prediction involves estimating the result variable using pre-selected predictors and applying the fully trained model to its intended output. In this study, the intention to utilize family planning was ascertained using the top predictors identified during the analysis. Consequently, the most accurate classifier will determine, to a predetermined degree of accuracy, whether or not a woman intends to use family planning.

Model interpretation/explanation using Shapley Additive exPlanations (SHAP)

An interpretation and explanation of any machine learning model's prediction can be found globally or locally using Shapley Additive exPlanations (SHAP) analysis. The fundamental concept behind the SHAP analysis is figuring out each predictor's marginal contribution to the outcome variable prediction result. In machine learning, features are chosen using SHAP values. The aggregate Shapley value of each characteristic for each sample should be shown in order to assess the impact of each predictor on the prediction of intention to use family planning. Moreover, a waterfall plot was used to illustrate how each component contributed to the prediction of a positive class. Finally, the overall data preparation and analysis process is presented in (Fig. 1). A thorough explanation of the SHAP analysis can be found in the previous article (36)

Socio-demographic and economic characteristics

The study included a weighted sample of 5993 women who were of reproductive age. The mean age of the sample was 23.89 years (± 19.2 standard deviation), with the majority of the women, 5,898 (71.87%), ranging between the ages of 15 and 19. 5120 (63.42%) of the participants were married or living with their spouses, while 4,957 (60.41%) were rural residents. Muslims made up 31.91% of the attendees, while Orthodox Christians made up the majority with 3,068 (38.00%). The majority of women, 2,595 (32.14%), have completed secondary school or above (Table 1).

Table 1

socio-demographic status of reproductive age women in Ethiopia, 2021
Variable	Categories	Weighted Freq.	Percent (%)
Residence	Urban	3,249	39.59
Residence	Rural	4,957	60.41
Marital status	Married	5,120	63.42
	single	2,220	27.50
	Divorced/widowed	733	9.08
Women’s age	15 to 19	5,898	71.87
	20 to 29	1,317	16.05
	30 to 39	712	8.68
	40 to 49	279	3.40
Education level	No education	2,421	29.99
	Primary	3,057	37.87
	secondary and above	2,595	32.14
Wealth status	Poor	2,742	33.42
	Middle	1,386	16.89
	Rich	4,077	49.69
Media access	No	5,458	67.78
Media access	Yes	2,594	32.22
Religion	Orthodox	3,068	38.00
	Muslim	2,576	31.91
	Protestant	2,363	29.27
	Others*	66	0.82
Note: others* indicates: catholic, Wakefata, atheist and traditional

Regarding the regional distribution of respondents, The Amhara region 1,584(19.30%), the Southern Nations, Nationalities, and Peoples' region 1,322 (16.11%), and the Oromia region accounted for the majority of respondents (1,858 (22.64%). No individuals from the Tigray regional state were in the study due to the conflict in the area throughout the study period (Fig. 2).

Reproductive health and Family planning service characteristics

Of the total respondents, 5,381 (60.41%) had previously been pregnant, and about 11.82% of the women (49.13%) had unmet family planning needs. About (48.3%) of the respondents had never utilized any family planning techniques. approximately 4,540 (74.69%) of the women started experiencing sex between the ages of 15 and 19, and 5.85% of the individuals said that their partner forced them into getting pregnant (Table 2).

Table 2

Reproductive health and family planning service characteristics of study participants
Variable	Categories	Weighted Freq.	Percent (%)
Ever been pregnant	No	2,691	33.34
Ever been pregnant	Yes	5,381	60.41
Ever used family planning	No	3,876	48.31
Ever used family planning	Yes	4,148	51.69
Visited a facility for care for self or children in past 12 months	No	4,184	51.96
	yes	3,868	48.04
Current used of any contraceptive methods	No	5,933	74.27
Current used of any contraceptive methods	Yes	2,055	25.73
unmet need for family planning	No	7,116	88.18
unmet need for family planning	Yes	954	11.82
Your partner tried to force you to become pregnant	No	4,815	94.15
Your partner tried to force you to become pregnant	Yes	299	5.85
Was visited by HEW who talked about FP in past 12 months	No	7,370	91.59
Was visited by HEW who talked about FP in past 12 months	Yes	677	8.41
Age at first sexual intercourse	15 to 19	4,539	74.69
	20 to 29	1,478	24.32
	30 to 39	55	0.91
	40 to 49	5	0.08

Intention to use family planning among reproductive age women in Ethiopia, based on PMA survey, 2021

Among the study participants, 2,891(48.24%) reproductive age women had no intention of using any form of family planning (Fig. 3).

Machine learning approach to identify predictors of intention to use family planning among reproductive age women in Ethiopia, 2021 PMA survey

Balancing data and Model performance comparison

In order to balance the outcome variable's unequal distribution, 168 additional synthetic observations from the minority category those who did not intend to utilize family planning were produced by the SMOTE oversampling approach. In order to create symmetric distributions for both categories and enable the development of reliable prediction models, the total intention to use status distribution was adjusted from 2313 not intended to use and 2481 intended to use, resulting in 2481 in each class.

To assess the performance of the models to predict intention family planning among reproductive age women, a stratified 10-fold cross-validation was employed, with particular consideration paid to mean accuracy and mean area under the curve score. Stratified 10-fold cross-validation was used to assess classifiers on the unbalanced training data, and the support vector machine emerged to the top with an accuracy of 76.7% and 84.2% areas under the ROC curve. The unbalanced class character of the outcome variable, which could skew the model in favor of the majority class, makes this finding potentially unreliable. After balancing the training data using the SMOTE oversampling technique, an ML model comparison was conducted in order to prevent this biased model creation. Accordingly, with a 76.8% accuracy rate and an 84.6% area under the ROC curve, Random Forest emerged as the most accurate predictive model to predict intention to use family planning among reproductive age women in Ethiopia (Table 3).

Table 3

Model comparison through tenfold cross-validation on training data
Models	Performance	Unbalanced data	Balanced data
Logistic regression	Accuracy (%)	76.3	75.7
Logistic regression	AUC (%)	83.1	833
SVM	Accuracy (%)	76.7*	76.7
SVM	AUC (%)	84.2*	84.3
XGBoost	Accuracy (%)	75.3	76.3
XGBoost	AUC (%)	82.9	83.8
KNN	Accuracy (%)	72.5	72.2
KNN	AUC (%)	78.6	78.7
Random forest	Accuracy (%)	76.1	76.8*
Random forest	AUC (%)	83.5	84.6*
AdaBoost	Accuracy (%)	75.7	75.9
AdaBoost	AUC (%)	82.4	83.2
Naïve Bayes	Accuracy (%)	72.5	71.7
Naïve Bayes	AUC (%)	77.3	77.2
Artificial neural network	Accuracy (%)	72.6	73.5
Artificial neural network	AUC (%)	79.6	80.7

Hyper-parameter tuning of Random Forest

Scikit-learn do not always provide the best solution for a given problem, even if it offers a set of reasonable default hyper-parameters for all models, including Random Forest. The number of decision trees in the forest (n_estimators), the number of features that each tree considers when splitting a node (max_features), the minimum number of samples needed to split an internal node (min_samples_split), the minimum number of samples needed to be at a leaf node (min_samples_leaf), and the number of samples to draw from independent variables to train each tree (max_samples) were among the hyper-parameters that were optimized with one hundred seventy five trials on a specified search space using stratified 10-fold cross-validation in order to maximize the performance of random forests. The Scikit-learn default hyper-parameters and our adjusted hyper-parameters are displayed in (Table 4).

Table 4

Default and optimal tuned hyper-parameters of Random Forest model
Hyper-parameters	Default	Optimal value
Number of tree	100	175
Number of features considered for the best split	Square root of the number of features	0.302
minimum number of samples required to split an internal node	2	9
minimum number of samples required to be at a leaf node	1	1
number of samples to draw from independent variables to train each base estimator	None	1

Finally, random forest model was created with these tuned hyper-parameters on balanced training data through 10-fold cross-validation and yielded 77.0% accuracy and 85% areas under the curve.

Feature selection

Following the selection of the best model, previously unseen test data was used to predict the intention to utilize family planning. Following random forest training on unbalanced training data, balanced data using default model parameters, and a comparison with an optimized model trained on balanced data, the prediction was made. Following balanced and unbalanced data training for the random forest model, the prediction on unseen test data yielded an area under the ROC curve score of 0.83 for both. An improved AUC of 0.85 was anticipated by hyper-parameter-tuned random forest, nevertheless. In this study, the top predictors of the intention to use family planning were determined using model-agnostic SHAP global feature importance. In order to quantify a feature's contribution to the anticipated intention to use family planning among reproductive age women in Ethiopia, this technique looks at the mean absolute SHAP value for each predictor over all of the data. Higher mean absolute SHAP values indicate greater influence of the predictors, which are arranged in descending order of their impact on the outcome variable prediction.

The findings showed that the most significant predictors of women's intention to use family planning were ever been utilized family planning (fp_ever_used_1), a partner or husband older than 40 (partner age_2), being single (marital_status_5), and being Muslim (religion_4). Other significant predictors of intention to use family planning included being pregnant (pregnant_1), having ever been pregnant (ever_pregnant_1), need to have more children (more_children_pregnant_2), having a son or daughter relationship to the head of the household (relationship_3), and unmet need for spacing and limiting. Each class's horizontal rectangle is half-filled with the colors red and blue, as it’s shown in the Fig. 6. As a result, each feature has an equal influence on whether a case is classified as intended to use family planning (label = 1) or not intended to use family planning (label = 0) (Fig. 4).

Model interpretation/explanation

A comprehensive picture of how the factors affect the model's predictions across the board was shown by using Beeswarms plots. Plotting the Shapley value of each individual predictor for each sample, Fig. 7 shows the distribution of the impacts of each predictor on the model's output (i.e., intention to use family planning prediction).

The points on this beeswarm plot represent Shapley values of the features related to intention to use family planning status, providing insight into the importance and association of each of the top ten features on the outcome variable. The red and blue hues in the figure represent the higher and lower values of each predictor’s variable. Points that are right to the vertical line (0 SHAP value) increase the likelihood of intention to use family planning while the left side decreases likelihood of intention to use family.

Since all of the variables are categorical and have two categories, the blue line denotes category code 0 (low value) and the red line denotes category code 1, which is a high value. Therefore, having a partner or spouse who is older than 40 (partner_Age_2), being a Muslim (Religion_4), having ever been pregnant (ever_pregnant_1), and being older than 30 while using family planning for the first time will increase the likelihood of intending to use family planning.

Contrarily, the likelihood of intending to use family planning decreases with the following factors: having ever used family planning (fp_ever_used_1), being single (marital_status_5), having a partner between the ages of 21 and 39 (partner_age_1), being pregnant (pregnant_1), need to have more children (more_children_pregnant_2), and no unmet need for spacing and limiting (Unmate_need_for_spacing_limiting_1) (Fig. 5).

Tenfold cross-validation was used to train eight machine learning classifiers on balanced and imbalanced training data. Random Forest was found to be the most accurate predictive model, and it was optimized for its optimal hyper-parameters before more analysis was carried out. Based on the result of this study, 51% of reproductive age women in Ethiopia had intention of using any form of family planning. This finding is higher than the result of a study conducted on Ethiopian Demographic and Health Survey, 2011 (44.1%) (37) but lower than the result of studies conducted in 90%(38), Gahanna (39) and Sodo(40) the prevalence was found to be 70%, Addis Ababa 60% (41). The reason behind the lower magnitude of intention to use contraception was the difference in availability, accessibility, infrastructure, and socioeconomic status between the present study, and a study done in America.

The SHAP analysis revealed that age at first family planning use, a partner or husband older than 40, being single, being Muslim, being pregnant, having previously been pregnant, need to have more children, having a son or daughter relationship to the head of the household and need for spacing and limiting were significant predictors.

According to the SHAP model explanation being older than 30 while using family planning for the first time will increase the likelihood of intending to use family planning. This finding is in line with the finding of study conducted in Addis Ababa (42) and Debremarkos (19) which founds an increase in intention to use family planning as age increases while using family planning for the first time. This could be because, at this age, women are headed toward menopause and the desired number of children is reached, discouraging them from having more children and increasing their intention to use family planning. However, this finding is in contradiction with a study conducted in Aksum (43) which founds the decrease in intention to use family planning with an increase in age.

Similarly, having ever been pregnant increases the likelihood of intending to use family planning. This result was in line with the findings of a study conducted in Adigrat town, Northern Ethiopia (19) and Aksum (43). This could be due to; experiencing discomfort and pain during pregnancy and having more children could increase their need for spacing, which, in turn, would increase their intention to use family planning

Additionally, having a partner or spouse who is older than 40 years will increase the likelihood of intending to use family planning. The finding is consistent the result of Bangladesh Demographic and Health Survey (44) and the study done at rural Dembia District (45). This could be due to the reason that Aged husbands may better share decision-making autonomy with their wives and approve the utilization of modern contraceptive utilization. Husband’s age is also related to better household income which has a positive impact on modern contraceptive utilization.

In this study, being single decrease the likelihood of intending to use family planning. This result was consistent with studies conducted in Tanzania(46) and Gondar Town(47). The result highlights the value of male involvement in reproductive health issues, such as fertility and contraception, and couple motivation through education. However, this finding was not compatible with the finding of the study in Mali(48). The two nations' differing socio-demographic compositions and the status of husbands' habits could be the cause.

Similarly, having ever been utilized family planning will decrease the likelihood of intending to use family planning among reproductive age women in Ethiopia. Similar finding was documented in studies conducted in Malawi (49), Gondar (50) and North Ethiopia ((51). The reasons behind this could include adverse reactions, rejection from spouses, and concerns about the composition of their breast milk changing due to their prior usage of family planning. The priority should thus be on reducing side effects, increasing comfort with use, and ensuring the support of partners for continued use of family planning.

In this study need to have more children found to decrease the likelihood of intending to use family planning among reproductive age women in Ethiopia. This finding is supported by the study conducted in the Pakistan (52), Bangladesh(53) Gondar city(54), and North Shoa Zone (55). It was clear that women who wanted to have children were not prepared to use family planning.

Similarly no unmet need for spacing and limiting was found to decrease the likelihood of intending to use family planning among reproductive age. This finding is in line with the result of cross-sectional study conducted in Ethiopia(36). Unmet needs for family planning arise when a woman of reproductive age, single or married, does not use any kind of birth control despite her desire to stop or postpone having children. Consequently, it follows that a woman who has no unmet needs for spacing and limiting her children's birth may not intend to use family planning.

Strength and Limitations of the study

The largest problem with utilizing black-box machine learning models, like Random Forest, is that it becomes more difficult to understand the outcomes and the variables influencing the predictions. The researchers employed further investigations to ascertain how factors increased or decreased intention to use family planning to reduce the interpretation limits of machine learning results, which are a result of their black-box nature. In order to evaluate each predictor's relative significance and learn more about how each element affected the model's predictions, the researchers employed a variety of methodologies, including SHAP. Because of this, we were able to comprehend how different factors influenced the model's predictions. Although SHAP explanations are useful for understanding specific forecasts or illustrations, their localized nature limits their ability to provide a global understanding of models. As a result, model behavior or patterns cannot be fully captured by SHAP explanations.

The results of this investigation showed that reproductive women had low intentions to use family planning. It is therefore highly recommended that the intention of family planning use and initiation of family planning provision should become a standard of service delivery to achieve the 2030 SDGs.

This study aimed to identify the major determinants that influence women's intention to utilize family planning. Eight machine learning algorithms were trained to predict the intention to use, and the accuracy and area under the curve of each algorithm were used to assess its performance. The most accurate model, with 77.0% accuracy and 85% areas under the curve, was the random forest model using tenfold cross-validation.

Using the SHAP feature importance technique, the top 10 predictors of intention to utilize family planning were found, and their implications were investigated. Lastly, the SHAP analysis showed that having a partner or spouse who is older than 40, being a Muslim, having ever been pregnant, and being older than 30 while using family planning for the first time will increase the likelihood of intending to use family planning. In contrast, the likelihood of intending to use family planning decreases with the following factors: having ever used family planning, being single, having a partner between the ages of 21 and 39, being pregnant, need to have more children, and no unmet need for spacing and limiting. Therefore, family planning providers should emphasize reducing barriers of intention like side effect and discomfort with the family planning method, targeting unmet need for family planning, and the promotion of husband / partner involvement and family planning use.

AUC

Area under the curve

EDHS

Ethiopian Demographic and Health Survey

KNN

K-nearest neighbor

Logistic regression

Machine learning

Random forest

ROC

Receiver operating characteristic

SDG

Sustainable Development Goals

SMOTE

Synthetic Minority Oversampling Technique

SSA

Sub-Saharan Africa

SVM

Support Vector Machine

WHO

World Health Organization

XGBoost

EXtreme gradient boosting

Ethics approval and consent to participate

Permission to use the data has been granted by the PMA Ethiopia’s survey project through legal registration. The data was used which is available on the public domain through the PMA website (https://www.pmadata.org/data/request-access-datasets) and can be accessed upon reasonable request after creating an account.

Competing interests

The authors have declared that no competing interests exist.

Contributions

JBA, TDN, ADW, DNM, SJW, EBE AND SDK were made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; and agree to be accountable for all aspects of the work. All authors read and approved the final manuscript.

Funding

The authors received no specific funding for this work.

Author Contribution

Acknowledgments

We are appreciative of the PMA survey project for granting us a license to access the data so that we could carry out the investigation. Without the Python community, this analysis would not have been feasible. Building data analysis pipelines is quick and simple with the many open-source modules and frameworks created by the Python community. This analysis would have been far more challenging and time-consuming without these tools.

Data Availability

The datasets analyzed in the current study are available in the Performance Monitoring for Action repository, https://www.pmadata.org/data/request-access-datasets.

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Explainable machine learning algorithm to identify predictors of intention to use family planning among reproductive-age women in Ethiopia: Evidence from the performance monitoring and accountability (PMA) survey 2021 dataset

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Abstract

Introduction:

Method

Result

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Introduction

Method

Study design

Source and study population

Sample size

Study variables

Dependent variable

Predictor variables

Data processing and analysis

Data source/collection

Data preparation/pre-processing

Model selection and training

Model evaluation and Hyper-parameter tuning

Prediction

Model interpretation/explanation using Shapley Additive exPlanations (SHAP)

Results

Socio-demographic and economic characteristics

Reproductive health and Family planning service characteristics

Balancing data and Model performance comparison

Hyper-parameter tuning of Random Forest

Feature selection

Model interpretation/explanation

Discussion

Strength and Limitations of the study

Conclusions

Abbreviations

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Competing interests

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Funding

Author Contribution

Acknowledgments

Data Availability

References

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