Leveraging machine learning to predict mosquito bed net utilization among women of reproductive age in sub-Saharan Africa

(2025) 24:317 Baykemagn et al. Malaria Journal https://doi.org/10.1186/s12936-025-05563-8 Malaria Journal Open Access RESEARCH Leveraging machine learning to predict mosquito bed net utilization among women of reproductive age in sub‑Saharan Africa Nebebe Demis Baykemagn1*, Tesfahun Zemene Tafere2, Getachew Teshale2, Andualem Yalew Aschalew2, Melak Jejaw2, Kaleb Assegid Demissie2, Azmeraw Tadele3, Asebe Hagos2, Misganaw Guadie Tiruneh2 and Jenberu Mekurianew Kelkay4 Abstract Background Malaria remains a major public health challenge, particularly in sub-Saharan Africa, where women of reproductive age are especially vulnerable during pregnancy and childbirth. To identify key predictors and improve predictive accuracy, machine learning algorithms such as Random Forest were applied, along with SHAP analysis, to a large multi-country DHS dataset, with class imbalance addressed using Tomek Links and Random Over-Sampling. Methods This study employed a weighted dataset of 153,015 participants from the Demographic and Health Survey (DHS) conducted across ten sub-Saharan African countries. Data preprocessing and analysis were carried out using STATA version 17 and Python 3.10. Feature scaling was applied to standardize numerical variables, ensuring uniform weighting across predictors and improving model stability. An 80:20 data split ratio was applied, and class imbalance was addressed using Tomek Links combined with Random Over-Sampling. Eight models were selected and trained using both balanced and unbalanced datasets. The model performance was evaluated using metrics such as ROCAUC, accuracy, recall, F1 score, and precision. Results The Random Forest algorithm performed best in this study, with an accuracy of 83%, an F1 score of 82%, recall of 80%, precision of 84%, and an AUC of 88%. Fifty-five percent of participants used mosquito nets. The SHAP analysis showed that Age above 34, being employed, frequent social media use, higher education, institutional deliveries, and female-headed households increased bed net use, while fewer ANC visits and being divorced decreased its use. Conclusion Age above 34, being employed, frequent social media use, higher education, institutional deliveries, and female-headed households increased bed net use, while fewer ANC visits and being divorced decreased its use. Strengthening social media use for health information, promoting women’s education, encouraging institutional delivery, motivate for antenatal care services, and providing support to socially and economically vulnerable women are essential strategies to enhance mosquito net utilization. Keywords Prediction, Women of reproductive age, Machine learning, Malaria, Mosquito net utilization *Correspondence: Nebebe Demis Baykemagn Full list of author information is available at the end of the article © The Author(s) 2025. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Baykemagn et al. Malaria Journal Page 2 of 11 (2025) 24:317 Background Mosquito bed nets serve as a protective physical barrier that prevents mosquito bites during the night [1]. Their high effectiveness, affordability, and ease of implementation make them a cornerstone of malaria prevention strategies [2]. Despite these advantages, malaria remains a major global public health concern, particularly in sub-Saharan Africa, where women of reproductive age (WRA) are disproportionately affected due to increased vulnerability during pregnancy and childbirth [3]. Malaria remains a major public health concern, with sub-Saharan Africa bearing the greatest burden. In 2023, approximately 94% of global malaria cases and 95% of malaria-related deaths occurred in the region [4]. Overall, ninety-four percent of all malaria cases and deaths were reported in Africa. According to the World Health Organization (WHO), one person dies from malaria every minute [5], and the disease causes the deaths of an estimated 200,000 infants and 10,000 women annually in Africa [6]. Malaria remains a serious public health concern, placing nearly half of the global population at risk of infection [7]. In recent years, both malaria cases and fatalities have risen beyond expectations, with evidence indicating that a significant portion of the population continues to live in malaria-endemic areas. In 2023, there were 23 deaths per 10,000 malaria cases [8]. Although mosquito bed nets are proven to be an effective method of malaria prevention, their consistent use remains below the desired level in many endemic regions, particularly in sub-Saharan Africa [9]. In some African countries, such as Uganda, mosquito bed nets are distributed every 3 years as part of ongoing malaria prevention campaigns [10]. However, the effectiveness of these campaigns is often limited by inconsistent usage and behaviour that affect regular bed net utilization [11]. According to previous evidence, lower education levels, limited health awareness, age, and the use of nets for other purposes are factors contributing to poor utilization of bed nets [2, 12]. The use of mosquito bed nets is one of the most cost-effective strategies for malaria control, capable of reducing the risk of malaria infection by up to 50%, improving quality of life, and increasing productivity [13]. Despite their proven effectiveness, the inconsistent use of bed nets among women in sub-Saharan Africa remains a major barrier to achieving the 2030 malaria prevention and control targets, which aim to: (i) reduce malaria mortality rates by at least 90%, (ii) eliminate malaria in at least 35 countries, and (iii) prevent the resurgence of malaria in all malaria-free countries [14]. This issue is particularly critical among women, as evidence indicates that 36% of them are exposed to malaria infection during pregnancy [15, 16], and given that women in Africa are more often responsible for managing their children at home compared to men [17]. Improving mosquito net utilization among women of reproductive age is crucial not only for their own health but (...truncated)