TL;DR
A region-specific malaria prediction model for clinical diagnosis that uses patient demographics and clinical symptoms has the potential to assist in the development of a clinically based malaria diagnostic system.
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.
Asthma is considered to be one of the most common chronic pulmonary diseases across the world, which calls for appropriate identification and risk assessment to enhance patients' prognosis. Machine learning approaches have proved to be effective means of analyzing patients' data to predict a disease. This paper is aime...
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...
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.
The results showed that early prediction of oral cancer using non-diagnostic epidemiological and behavioural features alone is difficult, which means that epidemiological data should be combined with clinical and biomarker-based data to develop more accurate early diagnosis systems.
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
