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 dis...
Susmitha A., Rakesh Kumar Pattanaik, S. K. Mohapatra et al.· 2026 International Conferenc...· 0 citations
It is demonstrated that ML particularly ensemble models can be used to significantly improve malaria diagnosis and 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
The Gradient Boosting Machine (GBM) model demonstrated superior performance on the test set, and its high specificity may reduce unnecessary confirmatory testing, while its core predictors offer biological insights into TB-associated inflammatory processes.
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
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.
Sweta Bhan, Ayushi Singh, Pankaj U. Ramteke et al.· International Journal of Com...· 0 citations
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
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