Jul 2026· International Journal of Community Medicine and Public Health· Vol 13, pp. 4737-4747· 0 citations· 45 references
TL;DR
The role of Artificial Intelligence in augmenting data-driven decision-making for malaria control and elimination efforts is explored, focusing on surveillance systems, enhancing the effectiveness of intervention strategies and optimizing the resource allocations.
Abstract
Malaria endures a significant part in public health concern, especially in tropical and subtropical regions. Traditional malaria control methods often face limitations with surveillance, diagnosis and efficient resource allocation. This review explores the role of Artificial Intelligence (AI) in augmenting data-driven decision-making for malaria control and elimination efforts, focusing on surveillance systems, enhancing the effectiveness of intervention strategies and optimizing the resource allocations. AI technologies, mainly machine learning algorithms and computer vision systems, demonstrate significant potential in improving malaria control outcomes. Key findings include increased accuracy in outbreak prediction, improved diagnostic precision through automated microscopy and optimized resource allocation reducing response times. Additionally, deep learning models are emerging as promising tools in identifying drug resistance patterns and personalizing treatment protocols. AI integration in malaria control programs offers substantial benefits for public health decision-making. In this article, we conducted a comprehensive review of peer-reviewed literature, analyzing AI applications in malaria control across key domains such as surveillance, diagnosis, treatment and resource management. However, effective implementation requires robust data infrastructure, ethical frameworks addressing algorithmic bias and sustained international collaboration. Future directions prioritize equitable access, capacity building and development of standardized evaluation metrics for evaluating AI-driven interventions.
It is argued that artificial intelligence encompassing machine learning and deep learning can help shift malaria control from reactive reporting toward predictive, precision public health, while cautioning that it is one enabler among many rather than a stand-alone solution.
M. S. Abdi, Abdirahman Mohamed Adan, N.I. Ahmed et al.· Frontiers in Digital Health· 0 citations
This study demonstrates the effectiveness of machine learning techniques in malaria risk prediction using clinical information and proposes a malaria risk prediction model using machine learning techniques based on clinical information to potentially improve early detection and treatment of malaria, ultimately reducing the burden of the disease.
Prabhat Kumar, Pragati Sahu, S. Priyadarshini et al.· 2 citations
Artificial intelligence has considerable potential to improve NTD diagnosis in low-resource settings, but successful adoption depends on trust, transparency, and usability, so a proposed framework provides a structured pathway for developing explainable AI systems that are technically robust, clinically meaningful, ethically responsible, and implementable within resource-constrained health systems.
D. C. Innocent, Rejoicing Chijindum Innocent, Increase Praise Innocent· Frontiers in Digital Health· 0 citations
Background Sub-Saharan Africa continues to shoulder the heaviest burden of malaria. The 2024 WHO malaria report highlighted that Africa contributed an alarming 94% of the global cases and 95% of the deaths. In the WHO African region, progress towards elimination and management of malaria is hindered by weak health systems, and lack of traditional diagnostic methods such as microscopy and malaria rapid diagnostic tests (mRDT). The primary aim of the study is to develop a machine learning (ML) ensemble model for malaria diagnosis using clinical and demographic data, tailored for resource-limited settings. Methods A retrospective study was conducted using 637 patient records from Gutu Mission Hospital and Gweru Provincial Hospital in Zimbabwe. Clinical symptoms (fever, chills, abdominal pain, headache and diarrhea) and demographic features (age, gender, residence and travel history) were analysed. Data preprocessing included handling class imbalance using Synthetic Minority Oversampling Technique (SMOTE) and feature selection using Recursive feature elimination (RFE). Seven individual ML models including Logistic regression (LR), Random Forest (RF), Decision Trees (DT), Gradient Boosting (GB), K-Nearest Neighbor (KNN), Naive Bayes (NB) and XGBoost were trained and evaluated on the malaria dataset. The individual models were further combined to build, train and evaluate ensemble models such as Bagging, Stacking, Soft Voting and AdaBoost. Model performance was assessed using accuracy, precision, confusion matrices, recall and F1score and AUR-ROC metrics. Results Clinical symptoms (chills: p=0.001, fever: p=0.003, diarrhoea: p=0.01, abdominal pain: p<0.001) were statistically significant predictors of malaria. Of the demographic factors, only travel history (p=0.02) showed significant association with malaria. Among the seven individual ML models, GB achieved the highest predictive performance (Accuracy = 0.94), followed by RF (Accuracy = 0.94%) and XGBoost (Accuracy = 0.93%). The stacking ensemble model outperformed all individual ML models and other ensemble models (bagging, soft voting and adaBoost) achieving accuracy = 0.96, precision = 0.95, recall = 0.98, F1 score= 0.96 and AUC-ROC = 0.98. Conclusion This study demonstrates that ML particularly ensemble models can be used to significantly improve malaria diagnosis. The integration of these models into a web-based application could provide a scalable and accessible diagnostic tool for healthcare workers in resource limited settings.
Panashe Nyengera, H. Takawira, F. Mlambo· medRxiv· 1 citation
Malaria remains a severe health problem in endemic regions because people lack adequate diagnostic tools, leading to delayed medical care and elevated death rates. This research introduces a dual-mode artificial intelligence system that uses two complementary models to enhance malaria pre-screening and diagnosis. The patient-centered model uses multivariate logistic regression to analyze biosignals, including heart rate, body temperature, and oxygen saturation, collected through a wearable sensor prototype and a mobile interface for symptom analysis. The system enables patients to begin self-assessment to determine their level of need before scheduling a doctor's appointment. The clinician-centered model represents a customized convolutional neural network that uses annotated microscopy images of red blood cells to achieve 94.84% accuracy, 95.71% precision, 93.87% recall, 94.78% F1 score, and 0.84 Area Under Curve (AUC). The patient model achieved 94.6% accuracy and an AUC of 0.985 using a 70/30 train-test split. These systems work together to create a layered diagnostic system that can operate independently or together to detect malaria at an early stage, especially in areas with limited resources. The findings demonstrate that wearable biosignal data integration with image-based deep learning can produce dependable, scalable, and user-friendly systems for malaria pre-screening. Keywords - malaria diagnosis, artificial intelligence (AI), convolutional neural networks (CNN), wearable biosensors, multivariate logistic regression
This framework illustrates, without any claim of clinical validity, how a leakage-safe ML pipeline and SHAP interpretability can be combined and rigorously self-audited; real patient-level data and external validation are required before any clinical inference is drawn.
David Chepkonga, A. Langat, Ebenezer Esenogho et al.· Asian Journal of Research in...· 0 citations
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