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.
Prediction of diseases at the right time and accurately is crucial for improving patients' health conditions and reducing costs of healthcare services. Therefore, machine learning approaches have recently become very popular in predicting diseases in patients. Dengue fever, which is a highly spreading mosquito-born disease caused by the virus, still poses a significant threat to human health in the world. In this paper, a novel dengue prediction approach using the novel Light Gradient Boosting Machine (LightGBM), which is a high-performing decision tree-based ensemble learning method, is introduced. Clinical symptoms, lab tests, and demographic information are considered as features to predict whether a patient has been infected with dengue fever. Experimental results prove that proposed LightGBM model yields better accuracy, precision, recall, and F1-score than traditional machine learning algorithms.
Susmitha A., Rakesh Kumar Pattanaik, S. K. Mohapatra et al.· 2026 International Conferenc...· 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
Background: Tuberculosis (TB) continues to be one of the most pressing health issues worldwide, and in 2024, 10.6 million new cases were reported, resulting in 1.3 million deaths. However, the early and accurate diagnosis is crucial to limit transmission but the existing means are expensive, time consuming, and impractical in resource-limited environments. The objective of the present study was to build a quick and low-cost diagnostic model based on the use of blood biomarkers routinely available in the laboratory. Tuberculosis (TB) continues to be one of the more deadly diseases, particularly in the developing world. This study is aimed at developing a prototype solution where primary signs, symptoms and risk factors of TB would be identified at an early stage and machine learning (ML) predictive algorithms would be applied to them.
Methods: Data for 818 confirmed TB cases and 2,618 healthy controls were analyzed in a retrospective manner. Since the dataset is imbalanced, the ROSE technique was used to balance the training set. Seven machine learning algorithms were trained, and feature selection was done using LASSO regression and forward selection to determine the most predictive variables. SHAP analysis was used to increase interpretability of the model, and the final predictive model was implemented as an interactive Shiny web application to make it easier to be used in the clinic.
Result: The Gradient Boosting Machine (GBM) model demonstrated superior performance on the test set, achieving an area under the curve (AUC) of 0.821, specificity of 85.7%, and sensitivity of 64.9%. SHAP analysis highlighted platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and platelet distribution width (PDW) as the most influential predictors. Adjusting the classification threshold to 0.24 improved sensitivity to 82.6% while maintaining acceptable specificity (58.9%), underscoring the model’s potential utility as a screening tool. The accompanying Shiny application enhances accessibility and practical deployment in clinical settings.
Conclusion: This study presents a robust and interpretable GBM-based diagnostic model leveraging routine hematological parameters to provide a rapid, low-cost TB screening tool suitable for resource-constrained environments. The model’s high specificity may reduce unnecessary confirmatory testing, while its core predictors offer biological insights into TB-associated inflammatory processes. The interactive web application facilitates integration into clinical workflows, supporting early detection and improved TB management.
Yahya Khan, Lisa Sullivan, Danial Khan et al.· World Journal of Biology Pha...· 0 citations
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
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.
Sweta Bhan, Ayushi Singh, Pankaj U. Ramteke et al.· International Journal of Com...· 0 citations
Drug-resistant tuberculosis (DR-TB) is one of the most serious global public health
challenges, leading to increased mortality, prolonged treatment duration, and higher
healthcare costs. Conventional methods for diagnosing drug resistance often require
considerable time, delaying the initiation of effective therapy. Recent advances in machine learning (ML) have created new opportunities for predicting drug-resistant tuberculosis
through the analysis of clinical, demographic, radiological, and genomic data. Machine
learning algorithms can identify complex patterns associated with the development of drug
resistance and assist clinicians in making faster and more informed clinical decisions. This
paper reviews the application of machine learning methods for predicting drug-resistant
tuberculosis, including commonly used algorithms, data sources, performance metrics, and
the major challenges associated with implementing these technologies in clinical practice.
Damelya Nurgozhaeva· European Open Science Space· 0 citations
