Stunting Incidence Segmentation: A Cluster Analysis Approach and Targeted Intervention Strategies

Childhood stunting remains a pressing public health issue in many developing countries, requiring multifaceted strategies for effective mitigation [1]. Stunting, defined as a height-for-age measurement falling below two standard deviations from the Child Growth Standards set by the World Health Organization (WHO), arises from a combination of factors, including malnutrition, inadequate sanitation, and underlying health conditions [2, 3]. Its causes are multifactorial, encompassing chronic malnutrition, inadequate sanitation, and poor health conditions. Effective mitigation demands precise health condition mapping to identify priority areas for intervention. Accurate regional data enables tailored prevention strategies, aligning resources with community-specific needs to optimize outcomes [4].

The WHO’s 2025 global nutrition target and Sustainable Development Goal (SDG) 2.2.1 aim to reduce the prevalence of stunting among children under five by 2025. Our findings suggest that targeting stunting at an earlier age, such as 3 to 6 months, can maximize the impact of interventions [5].

Stunting remains a significant public health challenge, particularly in countries with high levels of poverty and hunger, such as Indonesia. Characterized by impaired growth and development due to chronic malnutrition, stunting severely impacts children's cognitive and physical development, potentially leading to long-term social and economic consequences [6]. According to the 2022 Indonesia Nutrition Status Survey (SSGI), the national stunting rate has decreased to 21.6%. Despite this decline, the figure remains above the 20% threshold set by the WHO, indicating the need for continued attention to stunting as a serious public health issue.

In District X, Central Java, the prevalence of stunting showed an alarming increase from 15.8% to 18.2% in 2022. This condition emphasizes the need for more targeted and effective interventions. Traditional methods to overcome stunting [7], such as growth monitoring and nutritional supplementation, have proven less effective in adapting to dynamic field conditions. Therefore, integrating digital-based solutions and data-driven approaches into public health strategies has become increasingly important [8].

The stunting measurement report received by the health office is used to determine strategic decisions in handling conditions in the community [9-12]. Although this aids in decision-making to meet community needs, the health office faces significant challenges in delivering effective health counseling. One major issue is the limited availability of human resources and the lack of speakers familiar with local conditions, which reduces the effectiveness of extension material delivery [13]. Additionally, there is currently no specific, targeted content tailored to the unique needs and conditions of each region.

This issue is further exacerbated by the difficulties in effectively mapping the conditions of regional conditions, making it difficult to determine the process of developing appropriate extension materials. To address this problem, a more structured and data-driven approach, as well as grouping, is needed to ensure that counseling is not only on target but also delivered by competent experts with relevant and specific materials. UNICEF's report on stunting in South Asia highlights the importance of interventions that prioritize child feeding, women's nutrition, and household sanitation as priorities to prevent stunting [14, 15].

Several studies have also highlighted the potential of data-driven approaches in improving our understanding of stunting patterns and guiding intervention strategies [15, 16]. These findings emphasize the need for localized strategies in stunting prevention, which serve as a foundation for developing more relevant health counseling materials.

This study addresses these gaps by integrating behavioral and environmental factors, such as maternal health, family smoking habits, and access to clean water, into the K-Means clustering framework. Unlike prior research that primarily focuses on anthropometric measures, this approach provides a more localized and comprehensive perspective on stunting prevention strategies. Community-based interventions, particularly those targeting maternal health and nutrition, have proven effective in reducing stunting rates.

Stunting remains a critical public health challenge in District X, Indonesia, where its prevalence increased from 15.8% to 18.2% in 2022. Prevention efforts currently face significant challenges, including limited human resources, ineffective material delivery, and inadequate regional health mapping. This study aims to develop a data-driven approach using K-Means grouping and Elbow method to classify regions based on health conditions and risk factors. The study seeks to create targeted intervention strategies and locally relevant health counselling materials, ultimately supporting resource allocation and more effective policy decisions in stunting prevention efforts.

By utilizing K-Means clustering and the Elbow method, we seek to identify different groups of health conditions in toddlers, providing input for more targeted and effective intervention strategies. This input takes the form of outreach material that is appropriate to regional conditions. The application of data collection and machine learning techniques in the field of public health, particularly in the context of child malnutrition and stunting, has gained attention in recent years. Previous research highlights the potential of machine learning in analyzing public health issues, including stunting.

The K-Means algorithm, combined with the Elbow Method, has been recognized in healthcare studies as an effective tool for segmenting patient groups and predicting health conditions. A recent study [16] evaluates the inertia-based index to determine the optimal number of clusters in K-Means, while other research demonstrates the effectiveness of the Elbow method in optimizing KNN for stroke prediction [17]. Some studies have also identified four key risk factors for stunting in Indonesia and classified malnutrition status in children [18].

Nevertheless, there is still a gap in the application of clustering techniques for mapping health conditions specifically for stunting prevention at the local level. This research addresses this gap by applying K-Means clustering and the Elbow method to a unique dataset from the District X, covering various health indicators relevant to stunting. By focusing on the local context and integrating multiple health factors, this study aims to provide insights for more targeted and effective stunting prevention strategies at the community level [9].

While these studies have made significant contributions, there remains a gap in the literature regarding the application of clustering techniques to map health conditions specifically for stunting prevention at the local level. Moreover, the integration of multiple health indicators beyond traditional anthropometric measures in clustering analyses warrants further exploration.

Our study builds on this body of work by applying K-Means clustering and the Elbow method to a unique dataset from District X, incorporating a range of health indicators relevant to stunting. By focusing on a specific local context and considering multiple health factors, our research aims to provide insights for more targeted and effective stunting prevention strategies at the community level.

Although previous studies have explored clustering methods in healthcare, few have utilized localized data to develop stunting prevention strategies tailored to the unique health conditions of local communities. This study introduces a new approach by applying K-Means clustering and Elbow methods to specific local datasets from District X, Indonesia, to identify health risk groups that require more contextual stunting interventions.

This study combines indicators such as access to clean water, worm infections, and health history of pregnant women that have not been widely applied in clustering analysis for the context of stunting prevention. The resulting clusters provide a more accurate picture of the distribution of health risks and enable the development of more relevant and data-driven counseling strategies for each risk group in the local community. This approach enhances the effectiveness of counseling and resource allocation at the community level and has the potential to serve as a model for data-driven health interventions in other areas with similar characteristics.

This research aims to contribute to stunting prevention by applying data clustering techniques to map the health conditions of toddlers in District X. By utilizing the K-Means algorithm and the Elbow method, this research identified different health condition groups to inform more targeted intervention strategies. The outputs include extension materials tailored to regional conditions, providing a more effective approach to prevent stunting. The application of data collection and machine learning techniques in public health, particularly in addressing child malnutrition and stunting, has gained significant attention in recent years. Previous studies have demonstrated the potential of machine learning in analyzing public health issues, including stunting.