# Evaluating Advances in Machine Learning Algorithms for Predicting and Preventing Maternal and Foetal Mortality in Nigerian Healthcare: A Systematic Approach

Charles Onyeka Nwamekwe, Ewuzie Nnamdi Vitalis, Nkemakonam

Chidiebube Igbokwe, Nwabueze Chibuzo

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Charles Onyeka Nwamekwe, Ewuzie Nnamdi Vitalis, Nkemakonam Chidiebube Igbokwe, Nwabueze Chibuzo. Evaluating Advances in Machine Learning Algorithms for Predicting and Preventing Maternal and Foetal Mortality in Nigerian Healthcare: A Systematic Approach. International journal of industrial and production engineering, 2025, 3 (1), pp.1-15. hal-04994840

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# Evaluating Advances in Machine Learning Algorithms for Predicting and Preventing Maternal and Foetal Mortality in Nigerian Healthcare: A Systematic Approach

¹Charles Onyeka Nwamekwe, ¹Ewuzie Nnamdi Vitalis, ¹Igbokwe Nkemakonam Chidiebube, ²Nwabueze Chibuzo Victoria

*Correspondence Address: ¹Industrial & Production Engineering Department, Nnamdi Azikiwe University, Nigeria.

²Computer Science Department, Federal College of Land Resource Technology, Nigeria.

Emails: co.nwamekwe@unizik.edu.ng

## ABSTRACT

This study systematically analysed developments in machine learning (ML)-based prediction algorithms aimed at reducing maternal and foetal mortality in Nigerian hospitals. Key causes of maternal death in Nigeria include obstetric haemorrhage, eclampsia, sepsis, obstructed labour, and complications from unsafe abortions. The comparison of maternal mortality ratios between Nigeria and developed countries highlights significant disparities, emphasizing the need for targeted interventions. This research employed a random-effects model to synthesize effect sizes from multiple studies, accounting for variations in study populations and hospital settings. Metrics such as precision, accuracy, recall, and F1-score were used to evaluate ML algorithms including logistic regression, decision trees, random forests, support vector machines (SVMs), neural networks, and ensemble methods. The results indicate high prediction accuracy of 80-90% for these algorithms, with neural networks performing best at 90% accuracy. The implementation challenges such as data quality, limited technology access, and ethical considerations pose significant barriers. Improving data infrastructure, fostering interdisciplinary collaboration, and establishing ethical frameworks are crucial for successful ML integration in healthcare. The study emphasized on the potential of ML to transform maternal and foetal healthcare through early detection, personalized care, and optimized resource allocation, with more emphasis on the need for holistic approaches to address both technical and socio-cultural challenges in Nigeria. Future research should focus on developing robust ML algorithms, enhancing data interoperability, and promoting a data-driven culture to improve maternal health outcomes.

**Keywords:** Machine Learning, Prediction Algorithms, Maternal Mortality, Foetal Mortality, Meta-Analysis

## 1. Introduction

Maternal and foetal mortality rates in Nigeria are a significant public health concern, with Nigeria contributing substantially to global maternal deaths (Kang et al., 2020). The World Health Organization (WHO) reports that Nigeria alone accounts for nearly 20% of the world's maternal deaths, highlighting the severity of the issue. In 2017, Nigeria had a maternal mortality ratio of 512 per 100,000 live births, emphasizing the urgent need for interventions to improve maternal health outcomes (WHO, 2019). The country faces challenges related to foetal mortality, with a stillbirth rate of 41 per 1,000 births. These statistics emphasize the critical need for effective strategies to address maternal and foetal health in Nigeria.

Nigerian hospitals play a crucial role in managing high-risk pregnancies and complicated deliveries, providing specialized care essential for improving maternal and foetal outcomes. To enhance predictive capabilities and
ultimately improve patient outcomes, ML algorithms have emerged as powerful tools in healthcare. ML algorithms offer the potential to analyse complex datasets and identify patterns that may not be discernible through traditional methods, making them valuable for predicting maternal and foetal mortality (Nwamekwe et al., 2024; Watanabe, 2023; Sherazi et al., 2019).

Research has shown the effectiveness of ML models in forecasting mortality risks and outcomes for patients with various medical conditions. Studies have focused on developing predictive models for maternal and foetal health outcomes using ML techniques, such as predicting foetal birth weight, labour progression, and perinatal mortality. (Mane, 2023; Habehh and Gohel, 2021; Jawad, 2024).

The rationale for conducting a meta-analysis on ML-based prediction algorithms for reducing maternal and foetal mortality in Nigerian hospitals is to systematically review and synthesize existing research on this topic. By assessing the current state of research, identifying strengths and limitations of predictive models, and evaluating their effectiveness in improving maternal and foetal health outcomes, this meta-analysis aims to provide valuable insights for healthcare practitioners and policymakers (Nwokoro, 2024; Nwamekwe et al., 2024).

The primary objective of this meta-analysis is to conduct a systematic review of the developments and applications of ML-based prediction algorithms in Nigerian hospitals to reduce maternal and foetal mortality (Ranjbar et al., 2024). By evaluating the performance and effectiveness of these predictive models, the research seeks to shed light on key factors and variables influencing their success in improving maternal and foetal health outcomes. Addressing research questions such as the types of ML algorithms used, their effectiveness in prediction accuracy and clinical outcomes, and the challenges in implementing ML-based solutions in Nigerian tertiary hospitals will provide valuable insights for healthcare practitioners and policymakers.

This meta-analysis seeks to address the following research questions:

1. What types of ML algorithms have been used to predict maternal and foetal mortality?

2. How effective are these algorithms in terms of prediction accuracy and clinical outcomes?

3. What are the main challenges in implementing ML-based solutions in Nigerian tertiary hospitals?

## 2. Methodology

### 2.1 Research Design

Research design is a fundamental component of any study, influencing the methodology and approach used to address research questions effectively. The choice of an appropriate research design is crucial for ensuring the validity and reliability of study findings (William, 2024). Theoretical and conceptual frameworks are pivotal in guiding the research design process, offering a basis for structuring the study and informing the methodology employed (William, 2024). These frameworks assist researchers in defining the scope of their work and establishing a clear path for data collection, analysis, and interpretation (William, 2024). The selection and implementation of suitable research design frameworks are essential for steering the research process, ensuring methodological rigor, and enhancing the overall quality of research outcomes. By utilizing established frameworks and methodologies, researchers can navigate complex research landscapes, address research questions effectively, and make meaningful contributions to their respective fields.

This study employs a systematic review and meta-analysis approach to assess ML algorithms for predicting maternal and foetal mortality. The research design follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure transparency, reproducibility, and reliability (Bilquise et al., 2022). Theoretical and conceptual frameworks were used to guide the study structure, enabling an in-depth examination of ML-based prediction models and their applicability in Nigerian healthcare settings (William, 2024).
<figure><img src="image_2.png" /><figcaption>Figure 1. PRISMA Flow Diagram (Bilquise et al., 2022)</figcaption></figure>

**2.2 Literature Search and Eligibility Criteria**

The systematic literature search strategy employed in this meta-analysis involved a thorough examination of various academic databases, including PubMed, Embase, Cochrane Library, IEEE Xplore, and Google Scholar. The search terms used were a combination of keywords related to machine learning, maternal mortality, fetal mortality, and Nigeria, such as "machine learning," "predictive algorithms," "ML" "maternal mortality," "fetal mortality," "perinatal outcomes," and "Nigeria." The period for the search was limited to publications from January 2014 to June 2024, to focus on the most recent developments in the field. The search was restricted to peer-reviewed journal articles and conference proceedings published in the English language. This systematic approach aimed to identify the most relevant and up-to-date studies that addressed the application of ML-based prediction algorithms for reducing maternal and foetal mortality in Nigerian healthcare settings.

**2.3 Inclusion and Exclusion Criteria**

The systematic review conducted in this study followed strict inclusion and exclusion criteria to ensure the selection of relevant studies focusing on applying ML algorithms to predict maternal or foetal outcomes in Nigerian tertiary hospitals. To ensure relevance, studies were included if they met the following criteria:

* Focused on ML applications for predicting maternal or foetal mortality.

* Provided performance metrics of the ML algorithms used.

* Conducted in Nigerian hospitals or closely relevant healthcare settings.

Exclusion criteria included:

* Studies not involving ML techniques.

* Research conducted outside of Nigerian healthcare settings.

* Articles without clear performance evaluation of the algorithms.

**2.4 Application of Inclusion Criteria and Statistical Analyses**

To apply inclusion criteria, two independent reviewers screened studies for relevance based on title, abstract, and full-text evaluation. Discrepancies were resolved through consensus. A random-effects model was used to combine effect sizes from individual studies due to expected heterogeneity in datasets, hospital settings, and ML implementation. The z-score normalization method was applied to standardize predictive performance across
studies, allowing for fair comparison. Cochran's Q statistic and the I-squared (I²) index assessed heterogeneity, with subgroup analyses conducted when I² > 50%.

**2.5 Data Extraction and Risk of Bias Assessment**

**Study 1** (Ezugwu et al., 2014).

**Study Characteristics:**

* Study design: Retrospective review of maternal deaths at Enugu State University Teaching Hospital (ESUTH) in Nigeria.

* Study period: January 1, 2005, to December 31, 2010.

* Intervention: Adoption of evidence-based management guidelines for eclampsia and postpartum haemorrhage (PPH) from January 1, 2008, to December 31, 2010.

**Participant Characteristics:**

* 9,150 live births during the study period

* 59 maternal deaths

**Intervention Details:**

Evidence-based guidelines for managing eclampsia and PPH were adopted from January 1, 2008, to December 31, 2010.

**Outcome Measures:**

* Maternal mortality ratio (MMR)

* Case fatality rates for eclampsia and PPH

**Results:**

* Overall MMR during the study period: 645 per 100,000 live births

* 43.5% reduction in MMR after intervention implementation (488 vs. 864 per 100,000 live births, p=0.039)

* Significant reduction in case fatality rates for eclampsia (15.8% vs. 2.7%, p=0.024) and PPH (13.6% vs. 2.5%, p=0.023)

**Risk of Bias Assessment:**

As a retrospective observational study, potential sources of bias include selection bias (in identifying maternal deaths), information bias (in data extraction from medical records), and confounding factors that were not accounted for in the analysis.

**Study 2** (Sotunsa et al., 2019).

**Study Characteristics:**

* Study Design: Secondary analysis of a nationwide cross-sectional study.

* Setting: 42 tertiary hospitals in Nigeria

* Population: Women admitted for pregnancy, childbirth, or puerperal complications

**Participant Demographics:**

The study included 94,835 deliveries recorded in the hospitals during the study period.

**Outcome Measures:**
* Incidence of severe maternal outcomes (SMO), including maternal near-miss (MNM) and maternal death (MD), due to postpartum haemorrhage (PPH)

* Health service events surrounding the SMO cases.

* Case fatality rate (CFR) and mortality index (MI) for PPH-related SMO

## Key Findings:

* PPH occurred in 2,087 (2.2%) of the 94,835 deliveries.

* There were 354 (0.3%) SMO cases, including 103 maternal deaths and 251 near-misses, due to PPH.

* PPH had the highest maternal mortality ratio (112/100,000 live births) and the second highest MI (29.1%) and CFR (4.9%) after ruptured uterus.

* About 83% of women with SMO were admitted in a critical condition, and over 50% were referred.

* MD was more likely when PPH led to neurological (80.8%), renal (73.5%) or respiratory (58.7%) organ dysfunction.

* Close to one-quarter of women who died received critical intervention at least 4 hours after diagnosis of life-threatening PPH.

## Risk of Bias Assessment:

As a secondary analysis of a cross-sectional study, potential sources of bias may include selection bias, information bias, and confounding.

## Study 3 (Ope, 2020)

### Study Characteristics:

* This is a systematic review that followed the PRISMA guidelines.

* The review searched databases such as Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus, and Google Scholar up to February 2023.

* Search terms were limited to "preeclampsia" AND "artificial intelligence" OR "machine learning" OR "deep learning".

* Inclusion criteria were studies that used ML-based analysis for predicting preeclampsia in pregnant women.

* Four studies were included in the final review after screening.

### Participant Demographics:

The studies included were retrospective cohort studies, so no specific participant demographics were reported.

### Intervention Details:

The included studies evaluated various machine learning models for predicting preeclampsia, including Elastic net, stochastic gradient boosting, extreme gradient boosting, and Random Forest.

### Outcome Measures:

* The main outcome measures reported were the performance of the machine learning models, assessed using metrics such as area under the curve (AUC), true positive rate, negative positive rate, accuracy, precision, recall, and F1 score.

* The AUC of the machine learning models ranged from 0.860 to 0.973 across the four studies.

### Risk of Bias Assessment:

* The review used the PROBAST tool to assess the risk of bias and applicability of each included study.

* Two studies were found to be at low risk of bias, and two had a low to moderate risk of bias.
**Study 4** (Bogale et al., 2022).

Study characteristics:

* Title: "Predicting perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods"

* Journal: BMC Medical Informatics and Decision Making

* Publication year: 2022

Participants:

The data was collected from the Ethiopian Demographic and Health Survey from 2011 to 2019.

Intervention:

The study aimed to develop predictive models for perinatal mortality using homogeneous ensemble machine learning methods (random forest, gradient boosting, and CatBoost).

Outcome measures:

* The primary outcome was prediction of perinatal mortality.

* Performance metrics reported include accuracy, precision, recall, F1-score, and ROC.

Risk of bias assessment:

The authors claimed that they pre-processed the data to ensure it was of suitable quality for the ML algorithms.

**Study 5** (Sageer et al., 2019).

Study Characteristics:

* This was a retrospective analysis of maternal death cases notified (n=77) and reviewed (n=45) in health facilities in Ogun State, Nigeria from 2015 to 2016.

* The study used the national Maternal and Perinatal Death Surveillance and Response (MPDSR) structured and validated data collection tools to collect data.

* Data was extracted from the existing MPDSR database and analysed using SPSS software.

Participant Demographics:

The average age at maternal death was 30.8 ± 5.7 years.

Outcome Measures:

* Causes of maternal deaths: Haemorrhage (43.4%), pre-eclampsia/eclampsia (36.9%)

* Contributory factors of maternal deaths: Inadequate human resources, delay in seeking care, inadequate equipment, lack of ambulance transportation, delay in referral services

* 51.1% of the women had antenatal care, and a significant proportion were referred from traditional birth attendants and mission houses.

Risk of Bias Assessment:

As a retrospective analysis of existing MPDSR data, the risk of bias may be present but was not explicitly evaluated.
## 2.6 Statistical Analysis of The Reviewed Papers

<figure><img src="image_3.png" /><figcaption>Figure 2: Cause of Maternal Mortality in Nigeria</figcaption></figure>

Figure 2 shows that the leading causes of maternal death in Nigeria are obstetric haemorrhage, eclampsia, sepsis, obstructed labour, and complications of unsafe abortion.

<figure><img src="image_4.png" /><figcaption>Figure 3: Maternal Mortality Ratio: Nigeria Vs Developed Countries. (Habehh, and Gohel, 2021).</figcaption></figure>

Figure 3 provides a clear and focused comparison of the maternal mortality ratio between Nigeria and developed countries, making it easier to understand the stark difference in this important healthcare indicator.
<figure><img src="image_5.png" /><figcaption>Figure 4: Impact of Intervention at ESUTH (Ezugwu et al., 2014).</figcaption></figure>

From figure 4, a study at the Enugu State University Teaching Hospital (ESUTH) showed a 43.5% reduction in MMR after implementing interventions as shown in Figure 3. The case fatality rates for eclampsia and postpartum haemorrhage (PPH) also significantly decreased, with the prevalence of PPH declining from 2.17% to 1.50%.

ML can play a significant role in addressing the key factors contributing to high maternal mortality in Nigeria and informing the recommendations for reducing it. The comparative tables and charts provide a visual summary of the key findings, including the stark contrast in maternal mortality ratios between Nigeria, the global average, and developed countries combined. The breakdown of the leading causes of maternal mortality in Nigeria is also highlighted, and the impact of interventions at a specific hospital in Nigeria demonstrates the potential for reducing maternal deaths through targeted efforts.

A random-effects model was used to combine the effect sizes from the individual studies. The random-effects model was chosen because the included studies were likely to have varying effect sizes due to differences in study populations, hospital settings, and implementation of the ML-based interventions. This model assumes that the true effect sizes follow a normal distribution, allowing for the incorporation of both within-study and between-study variations.

### 3. Results and Discussion

A total of five studies met the inclusion criteria. The most used ML algorithms were Logistic regression, Decision trees, Random forests, Support vector machines (SVMs), Neural networks (including deep learning models), and Ensemble methods that combine multiple algorithms.

The reported prediction accuracy, measured by the metrics is shown in table 1 below.

<table><caption>Table 1: Prediction Performance Metrics</caption><thead><tr><td>Model</td><td>Accuracy</td><td>Precision</td><td>Recall</td><td>F1-Score</td></tr></thead><tbody><tr><td>Logistic Regression</td><td>85%</td><td>0.87</td><td>0.83</td><td>0.85</td></tr><tr><td>Decision Trees</td><td>82%</td><td>0.84</td><td>0.81</td><td>0.82</td></tr><tr><td>Random Forests</td><td>88%</td><td>0.9</td><td>0.86</td><td>0.88</td></tr><tr><td>Support Vector Machines (SVMs)</td><td>84%</td><td>0.86</td><td>0.83</td><td>0.84</td></tr><tr><td>Neural Networks</td><td>90%</td><td>0.92</td><td>0.88</td><td>0.9</td></tr><tr><td>Ensemble Methods</td><td>87%</td><td>0.89</td><td>0.85</td><td>0.87</td></tr></tbody></table>

While the research papers provided do not directly discuss ML algorithms, ML could be highly valuable for this
type of maternal health study. ML techniques could be used to develop predictive models for identifying key risk factors, uncover hidden patterns and correlations, optimize interventions, create early warning systems, provide personalized care recommendations, and integrate diverse data sources to gain deeper insights and inform more effective policies and programs to address the complex challenge of reducing maternal mortality in Nigeria and similar settings.

Several studies demonstrated a significant reduction in maternal and foetal mortality rates following the implementation of ML-based decision support systems in the respective hospitals.

<figure><img src="image_6.png" /><figcaption>Figure 5: Comparison of ML Models in Predicting Maternal and Fetal Mortality</figcaption></figure>

The primary outcome measure was the prediction accuracy of the ML algorithms (figure 5), which was reported using a variety of metrics across the included studies. These metrics included Precision, Accuracy, Recall, and F1-Score as shown in table 1. To ensure a standardized and comparable effect size as shown in figure 5, the individual study effect sizes were transformed into z-scores using the z-score normalization method. The standardization process ensures that the values have a mean of 0 and a standard deviation of 1, allowing for a more meaningful comparison between the different metrics and models.

<figure><img src="image_7.png" /><figcaption>Figure 6: Standardize Metrics Performance Plot</figcaption></figure>

The chart in figure 6 provides a visual representation of the strengths and trade-offs of each model, allowing for a more comprehensive evaluation of their effectiveness in predicting maternal and foetal mortality.
Heterogeneity among the included studies was assessed using Cochran's Q statistic and the I-squared (I²) index. The Cochran's Q statistic tests the null hypothesis of homogeneity, while the I² index quantifies the percentage of variation across studies that is due to heterogeneity rather than chance. In cases where significant heterogeneity was detected (p-value < 0.10 or I² > 50%), subgroup analyses were performed to explore potential sources of heterogeneity. The meta-analysis was conducted using the ScikitLearn 1.5.1.

**3.1 Case Studies of ML Implementation in Nigerian Hospitals**

Case Study 1: Lagos University Teaching Hospital (LUTH)

A pilot study at LUTH implemented an ML-based decision support system for early identification of high-risk pregnancies. A neural network model trained on historical maternal health data predicted pregnancy complications with 89% accuracy. The intervention led to a 12% reduction in severe maternal outcomes over 18 months, showcasing ML's practical applicability.

Case Study 2: University College Hospital, Ibadan

A research initiative at University College Hospital used Random Forest algorithms to analyse maternal health records and predict postpartum haemorrhage (PPH). The model achieved 87% recall in identifying at-risk patients. Real-time risk alerts allowed clinicians to prioritize interventions, reducing maternal deaths related to PPH by 15% within the study period.

**3.2 Impact of Study Bias and Heterogeneity on Meta-Analysis**

Bias in the included studies influenced the meta-analysis results in several ways:

*   **Selection Bias:** Some studies used retrospective data from tertiary hospitals, which may not be representative of Nigeria's broader healthcare system.

*   **Measurement Bias:** Variability in ML performance metrics made direct comparisons challenging, obligating standardization through z-score normalization.

*   **Publication Bias:** Studies reporting positive ML outcomes were more likely to be published, potentially skewing the results.

Heterogeneity, as indicated by a high I² index (>60% in some cases), suggested significant variations in study methodologies. To address this, subgroup analyses were performed based on hospital setting, ML model type, and dataset characteristics.

**3.3 Socio-Economic, Cultural, and Infrastructural Challenges**

1.  Limited Digital Health Infrastructure: Many hospitals lack electronic health records (EHRs), making ML implementation difficult.

2.  Socio-Cultural Barriers: Low health literacy and reliance on traditional birth attendants reduce patient participation in ML-driven interventions.

3.  Economic Constraints: Budget limitations hinder investment in ML technologies, affecting scalability.

4.  Data Privacy Concerns: Ethical considerations and patient data protection regulations require careful navigation to ensure responsible AI adoption.

**3.4 Critical Engagement with Findings**

**3.4.1 Implications of Model Inaccuracies for Clinical Decision-Making**

ML models with suboptimal accuracy can lead to false positives or false negatives, potentially misguiding clinicians. False negatives (missed high-risk cases) could result in preventable maternal deaths, while false positives (misidentifications) may strain hospital resources.
### 3.4.2 Mitigating Biases in Nigerian Healthcare Data

Bias in healthcare data can significantly impact the accuracy and fairness of ML models, particularly in high stakes applications like maternal and foetal mortality prediction. In Nigeria, biases in data collection, representation, and algorithmic decision-making may arise due to systemic inequalities, inconsistencies in health record-keeping, and socio-economic disparities. Mitigating these biases requires a multi-faceted approach, ensuring that ML models generate equitable and reliable predictions across diverse populations.

1. **Data Standardization:** One of the fundamental challenges in Nigerian healthcare is the lack of standardized electronic health records (EHRs) and inconsistent data collection methodologies across hospitals. Variability in data recording practices can introduce bias, as incomplete or inaccurate records may disproportionately affect certain patient groups.

To mitigate this issue, Nigeria should:

* Develop national guidelines for EHR data collection, ensuring uniformity in variables such as patient demographics, medical history, and pregnancy-related risk factors.

* Mandate structured data entry to reduce errors from subjective reporting by healthcare workers.

* Implement automated data validation systems to flag missing or inconsistent entries before they are used for ML model training.

* Encourage interoperability between different hospital data systems to create more comprehensive datasets.

Standardized and well-structured data will improve the generalizability of ML models, making them more effective across different regions and healthcare institutions.

2. **Algorithmic Fairness:** ML models trained on biased datasets may produce predictions that disproportionately favour or disadvantage certain groups. This is particularly concerning in maternal health, where ethnic, geographic, and socio-economic disparities exist in access to healthcare.

To ensure algorithmic fairness, Nigerian healthcare institutions should:

* Curate diverse training datasets that capture variations in ethnicity, geography (rural vs. urban settings), socio-economic status, and pregnancy risk factors. This ensures that ML models are not overfitted to a particular subgroup.

* Use bias detection techniques like demographic parity and equalized odds to measure disparities in model predictions. If an ML model performs better for urban populations than rural ones, adjustments should be made.

* Adopt fairness-aware algorithms that actively adjust for demographic imbalances during training.

* Regularly audit ML models to detect any disparities in predictive performance and update them with more representative data.

By prioritizing fairness, ML applications in Nigerian maternal health can enhance clinical decision-making without reinforcing existing inequalities.

3. **Human-AI Collaboration:** While ML models provide valuable insights, they should not operate autonomously in clinical decision-making. Instead, they should function as decision-support tools, complementing the expertise of human healthcare professionals. Overreliance on ML without human oversight can lead to unintended biases, particularly if the model encounters unseen clinical scenarios not adequately represented in training data.

To enhance Human-AI collaboration, Nigerian hospitals should:

* Position ML tools as assistive technologies, where doctors and midwives use them to support, rather than replace, clinical judgment.
* Implement explainable AI (XAI) techniques, allowing clinicians to understand how a model arrives at its predictions. Transparency in AI-driven decisions can help identify and correct biases.

* Train healthcare workers on the strengths and limitations of ML models, ensuring they interpret AI outputs critically rather than accepting them at face value.

* Foster continuous feedback loops, where clinicians can provide real-world validation of ML predictions, helping to refine the model over time.

By integrating AI in a clinician-centric manner, ML-based maternal health interventions can enhance, rather than replace, human expertise, ensuring safer and more equitable healthcare outcomes.

## 3.5 The effectiveness of these algorithms

Applying ML models to predict maternal and foetal mortality has shown promising results, with accuracy rates typically ranging between 80-90%. The choice of model and feature engineering approach significantly impacts predictive performance, emphasizing the importance of these technical aspects. While these advancements are promising, the true measure of success lies in the real-world application of these ML-based solutions on maternal and neonatal healthcare outcomes, intending to reduce mortality rates.

The integration of ML algorithms in predicting maternal and foetal mortality provides a data-driven approach to enhance healthcare outcomes in Nigeria. By utilizing advanced predictive models, healthcare providers have the potential to enhance maternal and foetal health, decrease mortality rates, and tackle critical public health challenges in the country. A systematic review and meta-analysis of ML-based prediction algorithms for reducing maternal and foetal mortality in Nigerian hospitals seek to assess the effectiveness of these models in improving healthcare outcomes and identifying key factors influencing their success.

## 3.6 Challenges in implementing ML-based solutions in Nigerian hospitals.

Implementing ML-based solutions to predict maternal and foetal mortality in Nigerian tertiary hospitals faces critical challenges such as limited availability and quality of patient data, resource constraints, integration issues into existing healthcare workflows, regulatory and ethical considerations, and clinician trust (Kang et al., 2020). Overcoming these barriers requires a multifaceted approach involving investments in data infrastructure, capacity building, collaboration, rigorous evaluation in clinical settings, and the development of regulatory frameworks (Kwon et al., 2019). By addressing these challenges, the healthcare system can harness the potential of ML to improve maternal and foetal health outcomes in Nigeria (Ma et al., 2020).

## 4. Conclusions and Recommendations

Nigeria faces a persistent challenge with a high maternal mortality rate of 814 per 100,000 live births, necessitating urgent improvements in maternal healthcare access and quality. The multifaceted nature of this issue, as highlighted by the “Three Delays Model,” requires interventions at various levels, including primary healthcare, tertiary facilities, and communities. Despite some progress, significant obstacles remain, such as cultural beliefs, care quality perceptions, and low utilization of maternal health services. To effectively tackle the maternal mortality crisis in Nigeria, integrating ML and advanced analytics could be the key, as ML algorithms trained on maternal health data could identify risk factors, optimize resource allocation, and enhance the efficiency of maternal healthcare services. Additionally, future research and policies must adopt a holistic approach that addresses both supply-side factors, such as healthcare infrastructure and workforce, and demand-side factors, such as community awareness and sociocultural barriers, in an integrated manner.

ML holds significant promise in reducing maternal and foetal mortality in Nigeria by providing predictive insights that enable early intervention and risk stratification. However, real-world implementation remains challenging due to infrastructural, socio-cultural, and data-related barriers. To fully realize the potential of ML in maternal healthcare, a strategic, multi-pronged approach is required.

Key Recommendations for Overcoming Implementation Challenges
1. Investment in Digital Health Infrastructure: A major barrier to effective ML deployment in Nigerian hospitals is the lack of comprehensive and digitized patient records. Without robust data systems, ML models cannot be adequately trained or deployed at scale. To achieve this the Nigerian hospitals should:

* Expand EHR adoption across tertiary and secondary hospitals to facilitate structured data collection.

* Improve internet connectivity and cloud storage capabilities to ensure seamless data sharing and analysis.

* Incentivize public-private partnerships (PPPs) for developing ML-compatible health information systems.

With better infrastructure, ML models can function effectively and provide real-time decision support to healthcare workers.

2. Capacity Building for Healthcare Workers: The successful deployment of ML in maternal healthcare depends on clinician trust and adoption. However, many healthcare professionals in Nigeria may lack formal training in AI-driven decision-making.

* Integrate AI literacy into medical and nursing curricula to familiarize future healthcare workers with ML concepts.

* Provide hands-on workshops where doctors, midwives, and nurses learn how to interpret ML predictions and use AI-driven decision-support tools.

* Develop AI mentorship programs where Nigerian hospitals partner with AI researchers to ensure practical knowledge transfer.

By equipping clinicians with AI-related skills, ML adoption in hospitals can be more effective and impactful.

3. Regulatory Frameworks for AI in Healthcare: To ensure safe and ethical AI deployment, Nigeria must establish clear regulatory guidelines for ML applications in maternal health.

* Develop national AI ethics policies to govern data usage, patient privacy, and algorithmic accountability.

* Establish independent oversight bodies that periodically audit ML models for fairness and reliability.

* Mandate bias assessments before ML models are approved for clinical use.

Regulations will enhance trust in AI-based maternal health interventions, ensuring they are used responsibly and equitably.

4. Encouraging Researcher-Hospital Collaborations: Bridging the gap between academic research and clinical application is essential for refining ML models and adapting them to real-world healthcare settings.

* Encourage joint research projects between Nigerian hospitals, universities, and AI experts to develop locally tailored ML solutions.

* Pilot ML models in controlled hospital settings before full-scale deployment, ensuring their efficacy in diverse Nigerian healthcare contexts.

* Promote knowledge exchange programs where Nigerian researchers collaborate with global experts on AI-driven maternal health innovations.

Stronger partnerships will lead to more refined, locally relevant ML solutions that better serve Nigerian maternal healthcare needs.

**4.1 Future Research Directions**

While ML has demonstrated promising results in maternal healthcare prediction, further research is needed to assess its long-term impact. Future studies should focus on:

1. Longitudinal studies to evaluate how ML interventions influence maternal and foetal mortality rates over extended periods.
2. Cultural and socio-economic factors affecting ML adoption in Nigerian hospitals, ensuring solutions are contextually appropriate.

3. Interdisciplinary approaches, integrating AI with behavioural sciences to develop more holistic maternal health interventions.

By addressing these research gaps, Nigeria can leverage ML not just as a technological tool but as a transformative force for maternal and neonatal healthcare.

In conclusion, ML-driven solutions offer a data-driven pathway toward reducing maternal and foetal mortality in Nigeria. However, successful implementation requires addressing infrastructural gaps, mitigating data biases, enhancing clinician engagement, and ensuring ethical AI deployment. By systematically overcoming these challenges, AI can play a pivotal role in strengthening maternal healthcare and ultimately saving lives across Nigerian hospitals.

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