Logistics Research Articles

Balancing nutrition and budgets: Socio-ecological impacts on nutritional environments of families with low incomes

BackgroundResearch on childhood nutritional environments, which are predictive of a variety of child and family health and wellbeing outcomes, has primarily focused on parenting behaviors and the home food environment. However, broader social and community factors play an important role in shaping nutritional environments during early childhood. Food security is a key example that is closely linked to nutrition-related health disparities, which can arise from constrained financial resources and material deprivations. Understanding parent and caregiver lived experiences related to social and community components of family nutritional environments in the context of economic constraints could help to support multi-level nutritional practices that are optimal for healthy child development. MethodSemi-structured in-depth interviews were conducted using a narrative approach with parents (n = 21) of pre-school aged children living near or below the poverty line. Participants were recruited from social and community service sites pertinent to maternal and child health in Philadelphia where health disparities are highly concentrated. Analysis was conducted using MAX QDA analytic software following a systematic approach for thematic analysis in narrative research. ResultsThree themes describe the feeding and eating environments in the home and community context: 1) Food Procurement and Meal Planning; 2) Family Togetherness; 3) Creativity, Variety, and Flexibility. Creative decision making and food procurement strategies responsive to child preferences were balanced by shopping to save and visiting multiple stores for the best foods and savings. Logistical (e.g., transportation, childcare) and emotional supports facilitated food shopping habits and routines, while lack of trusted childcare, reliable transportation, and social support barriers challenged feeding and eating decisions. ConclusionStrategies for improving cost and time-efficiencies could help support health-promoting nutritional environments of families with food insecurity. Building social and community capacity and facilitating connections to practical supports that include trustworthy childcare and peer support may be particularly important for this population.

Comparison between statistical and machine learning methods to detect the hematological indices with the greatest influence on elevated serum levels of low-density lipoprotein cholesterol

IntroductionElevated levels of low-density lipoprotein-cholesterol (LDL-C) is a significant risk factor for the development of cardiovascular diseases (CVD)s. Furthermore, studies have revealed an association between indices of the complete blood count (CBC) and dyslipidemia. We aimed to investigate the relationship between CBC parameters and serum levels of LDL. MethodIn a prospective study involving 9704 participants aged 35–65 years, comprehensive screening was conducted to estimate LDL-C levels and CBC indicators. The association between these biomarkers and high LDL-C (LDL-C≥130 mg/dL (3.25 mmol/L)) was investigated using various analytical methods, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) methodologies. ResultThe present study found that age, hemoglobin (HGB), hematocrit (HCT), platelet count (PLT), lymphocyte (LYM), PLT-LYM ratio (PLR), PLT-High-Density Lipoprotein (HDL) ratio (PHR), HGB-LYM ratio (HLR), red blood cell count (RBC), Neutrophil-HDL ratio (NHR), and PLT-RBC ratio (PRR) were all statistically significant between the two groups (p<0.05). Another important finding was that red cell distribution width (RDW) was a significant predictor for higher LDL levels in women. Furthermore, in men, RDW-PLT ratio (RPR) and PHR were the most important indicators for assessing the elevated LDL levels. ConclusionThe study found that sex increases LDL-C odds in females by 52.9 %, while age and HCT increase it by 4.1 % and 5.5 %, respectively. RPR and PHR were the most influential variables for both genders. Elevated RPR and PHR were negatively correlated with increased LDL levels in men, and RDW levels was a statistically significant factor for women. Moreover, RDW was a significant factor in women for high levels of HDL-C.The study revealed that females have higher LDL-C levels (16 % compared to 14 % of males), with significant differences across variables like age, HGB, HCT, PLT, RLR, PHR, RBC, LYM, NHR, RPR, and key factors like RDW and SII.

New framework of port logistics in the post-COVID-19 period with 6th-generation ports (6GP) model

ABSTRACT Since the outbreak of COVID-19, its impacts on the maritime transportation and logistics field have been multi-dimensional. In addition to the green shipping corridor proposed by the Clydebank Declaration in the United Kingdom in 2021, port digitalisation and decarbonisation of the maritime industry have become focal issues in the field. The industry needs a new framework to offset the negative impacts of the pandemic and to accommodate integrated technologies comprising of artificial intelligence (AI), blockchain, cloud systems, internet of things (IoT) and others, which have been applied to the industry. Having considered these circumstances, this paper aims to propose the 6th-generation ports model with smart port (6GP) as a new framework for the port logistics industry in the post-COVID-19 period. The proposed 6GP contributes to providing business development strategy and port development policy for stakeholders in the industry in the post-pandemic era reflecting focal challenges such as digitalisation, decarbonisation, sustainability and smart transformation. It also contributes to expanding port devolution theory from the fifth-generation ports (5GP) to 6GP.

Tracing the technological trajectories of smart logistics domain based on citation analysis and topic modelling

ABSTRACT The emergence and development of intelligent technology are triggering major changes in the logistics industry, and relevant scientific and technological research on smart logistics (SL) is emerging endlessly. However, there is no research that systematically explores SL from the technological perspective. Therefore, this article selects 47,253 documents retrieved from the Derwent Innovation Index for systematic analysis and provides a comprehensive understanding of the SL domain. We combine main path analysis (MPA) and LDA topic modelling approaches to identify technological evolution trajectories. Through three main paths, the key technologies and technological evolution directions in the SL domain are revealed. Through topic identification and topic correlation calculation, global topic popularity trends and local topic evolution paths are identified. Finally, this study summarises our findings, contributions, and limitations. This is the first attempt to detect key technologies in the SL domain through MPA and topic analysis, thereby guiding relevant researchers and enterprises.

Poultry Preslaughter Operations in Hot Environments: The Present Knowledge and the Next Steps Forward.

Poultry production faces significant challenges, including high feed prices, diseases, and thermal stress, which impact broiler welfare and productivity. Despite advances in cooling technologies and ventilation, preslaughter operations still lead to considerable losses. This review highlights the need for the improved management of thermal environments and animal logistics. Preslaughter operations typically involve fasting broilers for 8-12 h to reduce gastrointestinal contents and contamination. Following fasting, broilers are caught, crated, and transported. Stress levels vary based on distance and conditions, with manual catching often causing stress and injuries. Catching should occur during cooler periods to minimise these issues, and transport conditions must be carefully managed. Lairage, the waiting period after transport, should be kept short (1-2 h) in climate-controlled environments to avoid stress and deterioration. Proper handling and efficient unloading are essential to prevent injuries and reduce economic losses. Stunning methods, such as electronarcosis and a controlled atmosphere, aim to minimise suffering before slaughter, though practices vary culturally and religiously. Logistics and real-time monitoring technology are crucial for enhancing animal welfare during transportation. Effective planning and the optimisation of transport processes is vital for reducing stress and losses, especially with regard to rising global temperatures and production demands.

Pengaruh Perceived Overqualification dan Work Stress Dengan Moderasi Transformational Leadership Terhadap Innovative Work Behavior dan Turnover Intention

This research aims to analyze and explore the influence of two key factors, namely Perceived Overqualification and Work Stress, on employee Innovative Work Behavior and Turnover Intention in the context of logistics companies. In addition, this research considers the moderating role of Transformational Leadership in the relationship between Perceived Overqualification and Work Stress with Innovative Work Behavior and Turnover Intention. This research involved a survey of employees at a logistics company using a questionnaire, then the data collected was analyzed using quantitative methods with the population in this study involving 180 employees at a multinational logistics company that provides warehousing and distribution services in the Bekasi Regency area. The research results show that Perceived Overqualification and Work Stress have an influence on Turnover Intention. Apart from that, Transformational Leadership has been proven to moderate the relationship between Perceived Overqualification and Innovative Work Behavior. These results indicate that human resource management in logistics companies must consider Perceived Overqualification and Work Stress as important factors in promoting innovative work behavior that can reduce employee turnover intentions. This research has practical implications for logistics companies in developing more effective and efficient human resource management policies. Apart from that, this research also provides theoretical contributions in understanding the relationship between Perceived Overqualification, Work Stress, Transformational Leadership, Innovative Work Behavior and Turnover Intention in the context of logistics companies.

The Effects of Logistics Management Practice on Organizational Performance a Study of Win Water Company Wolaita Zone, Boditi Town, Snnpr, Ethiopia

The Effects of Logistics Management Practice on Organizational Performance a Study of Win Water Company Wolaita Zone, Boditi Town, Snnpr, Ethiopia

Artificial Intelligence in the Organization of Nursing Care: A Scoping Review.

The integration of artificial intelligence (AI) in the organization of nursing care has continually evolved, driven by the need for innovative solutions to ensure quality of care. The aim is to synthesize the evidence on the use of artificial intelligence in the organization of nursing care. A scoping review was carried out based on the Joanna Briggs Institute methodology, following the PRISMA-ScR guidelines, in the MEDLINE, CINAHL Complete, Business Source Ultimate and Scopus® databases. We used ProQuest-Dissertations and Theses to search gray literature. Ten studies were evaluated, identifying AI-mediated tools used in the organization of nursing care, and synthesized into three tool models, namely monitoring and prediction, decision support, and interaction and communication technologies. The contributions of using these tools in the organization of nursing care include improvements in operational efficiency, decision support and diagnostic accuracy, advanced interaction and efficient communication, logistical support, workload relief, and ongoing professional development. AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.

Carbon emissions reduction path of logistics industry in Hebei, China: based on internal factors

To explore the optimal carbon emissions reduction path for the logistics industry in Hebei Province, China, this paper considers internal factors such as logistics agglomeration, transportation structure, and technology innovation as regulatory measures. The system dynamics method is employed to simulate the effects of carbon emissions reduction for each factor. Then, based on examining the interactive relationships among these internal factors, the paper proposes the optimal carbon emissions reduction path for Hebei Province’s logistics industry under different sequences of implementing the three measures. The results indicate that logistics agglomeration, transportation structure, and technological innovation can contribute to varying degrees of carbon emissions reduction in logistics. By 2030, it is projected that logistics agglomeration will result in a reduction of 21,799 million tons of carbon emissions while transportation structure and technology innovation will lead to reductions of 23,459 million tons and 20,778 million tons respectively compared to the BAU scenario. Furthermore, intricate interactions exist among these three internal measures. The optimal path for reducing carbon emissions is TSI-AGD-LTI. Specifically, to optimize the transportation structure first, relieve the pressure of logistics carbon emissions aggravated by logistics agglomeration, promote the cross-regional transfer of logistics carbon emissions, and then improve the level of technology innovation to strengthen further the logistics carbon emissions reduction effect of transportation structure adjustment.

Increasing the Manifestation of the Sustainable Development Principles in Logistics Organizations

The logistics sector is increasingly pressured to integrate sustainable development principles to align with their corporate goals. This paper reviews theoretical and practical frameworks of the logistics industry to examine how service providers in the sector can achieve a balanced economic, environmental, and social sustainability. Businesses are purposely created to maximize profits; however, increased awareness of corporate social responsibility and environmental sustainability has made them to look beyond that. Logistics service providers that adopt sustainable practices stand to benefit from long-term cost savings, improved brand reputation, and enhanced stakeholder relationships. Performed research leads us to point out that IoT, big data and blockchain are a some of the digital enablers that improve sustainability. Such advancements are necessary to minimize carbon emission, enhance the transparency of supply chain, and minimize negative effects on environment. At the same time, the literature analyzes emphasizes the fact that collaboration between various actors also contributes to the creation of incentives for achieving the goal of creating sustainable logistics systems. The literature points to stakeholder theory whose focus is on the issues of transparency, cooperation, and novelty in managing supply chains. The model gives a structure for achieving business objectives of generating returns without considering environmental and social factors. Obtained results also highlights those policies such as tax incentives given to companies that meet environmental standards weigh in greatly. Such measures have the potential to steer the logistics sector towards more sustainable operations with a corresponding competitiveness and economic growth. However, it is important to point out that more radical changes are required for reaching the desired state of sustainable development. Consequently, continuous investment in sustainable technologies, supportive government policies, and stakeholder collaboration are key to fostering a profitable logistics industry

Raising data availability and quality for improved disruption and carbon footprint management through a novel approach to primary data sharing: Virtual Watch Tower / VWT

Abstract The global supply chain and logistics industry is a self-organizing ecosystem consisting of numerous actors that work together to move goods from end to end. The different stakeholders involved are usually interdependent organizations, like freight forwarders, carriers, terminals, and homeland security agencies and information exchange between them is required to coordinate the activities along the individual transport chains. However, the exchange of information has often been analog, flawed, late, and incomplete. New circumstances, like unprecedented supply chain disruptions, new regulatory requirements around greenhouse gas (GHG) emissions, and generally growing shipper expectations create an urgent need for improved data sharing between actors. New technologies, like digital platforms, networks, and architectures as well as social media, mobile, analytics, cloud, and internet of things (SMACT) have brought some improvements, but not the required holistic digital perspective required or expected. Averages and approximations are usually insufficient to close data gaps, e.g., only primary data allows for accurate GHG emission calculations. Primary data sharing is widely seen as the missing piece of the puzzle. Primary data is data from the source providing an accurate state and picture of a situation. Primary data sharing at scale requires a new form of digital collaboration. We propose a rethink of digital collaboration as a means for broader primary private data sharing for complete end-to-end datasets and data quality, particularly focusing on the sharing of data associated with both transport plans and progress made in the respective movements of goods for more better disruption and carbon footprint management through more accurate calculations of estimated times of arrival (ETA) and GHG emissions. We introduce an example for digital collaboration in end-to-end supply chains that is focusing specifically on primary data sharing. The new thinking around digital collaboration manifests itself in the Virtual Watch Tower / VWT initiative (www.virtualwatchtower.org). In 2022, RISE and Singapore Maritime Institute signed a collaboration agreement focused on innovation in shipping. The VWT initiative is the first collaborative project under the umbrella of this partnership. The VWT is led by RISE, A*STAR, and VTT. VWT is a community-driven, digitally empowered initiative, a cargo owner-driven, and terminal-centric approach for improved supply chain management. It is the users themselves who co-create and co-evolve the solution that they need. The initiative aims to create a community that shapes the digital tool (VWTnet) they need to reach the required higher levels of visibility. This, through primary data sharing across the supply chain ecosystem and between actors (VWT Users) participating in individual end-to-end transports (VWT Shipments). The VWT serves as an object of research to hone the new thinking and understand the implications of its implementation.

A multi-step mixed integer programming heuristic for warehouse layout optimization

Warehouse layout optimization is critical to inventory and logistics management in organizations. In many instances, limited warehouse space is a constraint and a barrier to expanding operations and increasing demand. We present a multi-step solution using mixed integer programming to improve space utilization and increase order retrieval and fulfillment efficiency. We present a real-world case study to demonstrate the applicability and efficiency of the proposed mixed integer programming heuristics at a large distribution center.

Analisis Manajemen Logistik Obat di Dinas Kesehatan Kabupaten Kolaka Tahun 2023

Good logistics management of medicines is one of the factors so that medicines can be managed well. The pharmacy unit of the Kolaka District Health Service has problems with inadequate storage or storage of medicines and delays in the arrival of medicines. The aim of this research is to determine the Analysis of Drug Logistics Management at the Kolaka District Health Service. The research uses qualitative methods through in-depth interviews, observations and observation guides. The results of the research show that at the drug planning stage using the consumption method, drug procurement using an e-catalog system, drug storage is still not good because the warehouse is not large enough for drug storage. Suggestions for the Kolaka District Health Service.

The implications of blockchain for logistics operations and sustainability

Empirical studies based on detailed, theory-based analyses are essential for a deep understanding of technology adoption. This study provides an overview of blockchain applications in logistics management, employing a comprehensive theoretical framework. Blockchain is considered a critical digital infrastructure for logistics operations due to its distinctive characteristics, including decentralization, transparency, immutability, real-time information sharing, reliability, and end-to-end visibility. These characteristics address many contemporary logistics challenges. The study introduces a research model that integrates the fit-viability model (FVM) and task technology fit theory (TTF), demonstrating blockchain's suitability for enhancing logistics operational functions and sustainability performance. To validate the model, data were collected from logistics managers of 576 companies and analyzed using partial least squares (PLS) regression. This research offers valuable insights for managers, policymakers, and decision-makers on practical challenges and potential solutions in logistics through the application of blockchain. Furthermore, the study demonstrates that the implementation of blockchain can improve the alignment, resilience, transparency, integration, and sustainability of logistics tasks.

Further assessment of structural and social determinant of health effects on breast cancer care timelines: A qualitative study.

121 Background: Time to treatment (TTI) delays result in increased morbidity and mortality for cancer patients. These treatment delays disproportionately occur within marginalized communities due to structural challenges with social determinants of health (SDoH), disengagement with the healthcare system and limited or low health literacy that impacts their ability to obtain timely, appropriate care. We conducted semi-structured interviews with patients treated for breast cancer about their care treatment trajectories to further understand barriers and facilitators for timely treatment from the patient perspective. Methods: We conducted semi-structured interviews with 30 patients in 2023 who received curative intent breast cancer treatment at Montefiore Medical Center from 2019 to 2023. Interviews addressed barriers and facilitators of timely TTI, perceived timeliness and salience of timeliness, emotional well-being, and perceptions of the healthcare team and communication. We analyzed interviews using framework analysis. Results: A total of 30 patients were interviewed (mean age 58.9 year ± 10.6, 75% Non-Hispanic Black, 25% Non-Hispanic White). The median TTI was 52.5 days (49, 62 IQR). First treatment modality included surgery (75%) and chemotherapy (25%). Many participants found significant emotional and logistical support through family, social networks, religion, and connecting with survivors. Several patients faced transportation issues hindered their ability to adhere to schedules, childcare challenges, and difficulty managing numerous appointments, with some wishing transportation services had been offered at diagnosis. Some participants experienced unique cancer presentation, required clearances, involvement of non-oncologic providers and consultants, and time for decision-making about treatment options that may have contributed to delays. Some patients faced financial burdens including personal expenses, family support needs, and difficulties accessing third-party assistance (i.e., short-term disability, Supplemental Security Income). Some patients valued mental health counseling and other resources like group therapy and social workers, though some wished for earlier, more specialized support, indicating gaps in the support timeline. A few patients expressed preference for longer TTI that afforded them time to make important decisions and seek opinions. Conclusions: The study revealed that many patients faced logistical, emotional, and informational challenges while awaiting treatment and highlighted a need for more tailored assistance and timely interventions. Findings from our work highlight the need for further study on how healthcare systems can better equip and engage patients early in the care timeline to avoid delayed TTI.

The mediating effect of green culture on the relationship between GHRM and green sustainable performance in Jordanian SME logistics companies

The paper aims to explore the mediating effect of green culture (GC) on the relationship between Green Human Resource Management (GHRM) and Green Sustainable Performance (GSP) in small and medium logistics companies in Jordan. With increasing attention to environmental issues and sustainability becoming crucial for the future of the logistics sector, GHRM practices are gaining importance in enhancing sustainable performance. This study focuses on how promoting GC within organizations can strengthen the effectiveness of GHRM initiatives to achieve better sustainable outcomes. A quantitative research method was used to gather data from SME logistics companies in Jordan through structured questionnaires. Data analysis was conducted using SPSS-29 and Smart PLS4. The results show that GHRM practices have a significant positive impact on GSP in Jordanian SME logistics companies. Key components of GHRM, such as green recruitment and selection, green training and development, and green reward and compensation, were found to be strong predictors of GSP. Furthermore, GC was identified as a mediator between GHRM practices and GSP, showing that GHRM practices contribute to the development of a GC in these organizations. The findings also reveal that a strong GC fully mediates the relationship between GHRM practices and sustainable performance, emphasizing the importance of cultivating a robust GC to enhance the positive effects of GHRM on organizational sustainability. These results provide insights for addressing gaps in sustainable practices within the logistics sector and offer recommendations for managers looking to implement green initiatives in their companies.

Innovative Solutions for Sustainable Logistics in Food Manufacturing Industries overcome Barriers and Challenges

This paper explores innovative solutions for sustainable logistics in the food manufacturing industry, focusing on how these strategies address key challenges. It examines how advancements in technology, logistics strategies, and eco-friendly practices contribute to sustainability while maintaining efficiency and profitability. The study analyzes data from companies that have adopted modern logistics solutions, such as AI-driven optimization, renewable energy, and circular supply chain models, supplemented by expert interviews to gain practical insights. The findings reveal that these solutions help reduce carbon emissions, minimize food waste, and optimize resource use. Companies that have implemented these strategies have successfully overcome common barriers such as high costs, infrastructure complexity, and regulatory challenges. While the research focuses on large-scale manufacturers, smaller companies remain under explored, suggesting the need for future studies on scalability and policy frameworks to support wider adoption. This paper offers a unique contribution by combining current innovations in sustainable logistics with practical industry challenges, providing actionable insights for food manufacturers and laying the foundation for further research into overcoming financial and technological barriers.

A Strategic Overview of the Indian Fashion Industry with apropos to Logistics and Sustainable Supply Chain Management

This research study emphasizes the social, environmental, and economic facets of the fashion supply chain and attempts to provide a thorough analysis of sustainability challenges. It aims to comprehend the effects of many sustainability programs and practices, assess the lessons gained from industry mistakes and attempts, and pinpoint areas needing more study and development. Using a qualitative approach, the paper thoroughly reviews case studies, industry white papers, and publications in peer-reviewed journals. Insights from current sustainability trends are integrated with analyses of top brands' sustainability policies, including Nike, Unilever, and Patagonia. The process involves investigating the best methods for incorporating sustainability into fundamental company operations, evaluating social and environmental sustainability efforts, and estimating the economic effects of these activities. The necessity of combining social, environmental, and ethical standards holistically, the significance of expanding sustainability initiatives beyond first-tier suppliers, and the crucial role of supply chain openness are some of the major conclusions. Notable instances include Nike's innovations in sustainable materials, Unilever's circular economy programs, and Patagonia's fair work policies. According to the report, incorporating sustainability into the operations has long-term financial advantages, including improved customer loyalty and brand reputation. This study offers fresh perspectives on the realworld difficulties and achievements of integrating sustainability into the fashion supply chain. It analyses industry standards thoroughly and offers feasible recommendations for businesses looking to improve their sustainability initiatives. The current collection of knowledge on the sustainability of the fashion supply chain is greatly enhanced by the addition of particular case studies and the investigation of cutting-edge technology and practices.

Machine Learning to Predict Interim Response in Pediatric Classical Hodgkin Lymphoma Using Affordable Blood Tests.

Response assessment of classical Hodgkin lymphoma (cHL) with positron emission tomography-computerized tomography (PET-CT) is standard of care in well-resourced settings but unavailable in most African countries. We aimed to investigate correlations between changes in PET-CT findings at interim analysis with changes in blood test results in pediatric patients with cHL in 17 South African centers. Changes in ferritin, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), albumin, total white cell count (TWC), absolute lymphocyte count (ALC), and absolute eosinophil count were compared with PET-CT Deauville scores (DS) after two cycles of doxorubicin, bleomycin, vinblastine, and dacarbazine in 84 pediatric patients with cHL. DS 1-3 denoted rapid early response (RER) while DS 4-5 denoted slow early response (SER). Missing values were imputed using the k-nearest neighbor algorithm. Baseline and follow-up blood test values were combined into a single difference variable. Data were split into training and testing sets for analysis using Python scikit-learn 1.2.2 with logistic regression, random forests, naïve Bayes, and support vector machine classifiers. Random forest analysis achieved the best validated test accuracy of 73% when predicting RER or SER from blood samples. When applied to the full data set, the optimal model had a predictive accuracy of 80% and a receiver operating characteristic AUC of 89%. The most predictive variable was the differences in ALC, contributing 21% to the model. Differences in ferritin, LDH, and TWC contributed 15%-16%. Differences in ESR, hemoglobin, and albumin contributed 11%-12%. Changes in low-cost, widely available blood tests may predict chemosensitivity for pediatric cHL without access to PET-CT, identifying patients who may not require radiotherapy. Changes in these nonspecific blood tests should be assessed in combination with clinical findings and available imaging to avoid undertreatment.

An Ensemble Learning Algorithm with Hyperparameter Optimization (ELAHO) Model for Early Detection of Maternal Health Risk (MHR) Level Using Machine Learning

Despite the advancement in the healthcare system, maternal health risk remains high, which is the most challenging aspect nowadays. There is a need to develop an effective model for early detection, monitoring, and prediction of maternal health risk levels during pregnancy. The machine learning intelligence model has proven its effectiveness and robustness in providing accurate and reliable prediction, analysis, and interpretation of medical data, reducing several risk factors for early diagnosis in healthcare. In this research work, we proposed an ensemble learning algorithm with a hyperparameter optimization (ELAHO) model using machine learning algorithms to improve its robustness, effectiveness, and model performance. The proposed method uses a hybrid model of logistic regression and support vector machines (LG-SVM) to predict maternal health risk levels during pregnancy. The method utilized Python software for training, testing, and validation. We evaluated the performance of our proposed model using accuracy, precision, sensitivity, f1-score, and ROC-AUC score. The proposed models outperformed the conventional models and achieved 100% predictive accuracy. The proposed approach has the potential to be adapted as an intelligence-monitoring system for early medical diagnosis during pregnancy. The proposed techniques will help medical professionals make quick decisions accurately and enhance monitoring to improve the level of care offered to pregnant mothers and their unborn children.