Background Ebola virus disease (EVD) plagues low-resource and difficult-to-access settings. Machine learning prognostic models and mHealth tools could improve the understanding and use of evidence-based care guidelines in such settings. However, data incompleteness and lack of interoperability limit model generalizability. This study harmonizes diverse datasets from the 2014–16 EVD epidemic and generates several prognostic models incorporated into the novel Ebola Care Guidelines app that provides informed access to recommended evidence-based guidelines. Methods Multivariate logistic regression was applied to investigate survival outcomes in 470 patients admitted to five Ebola treatment units in Liberia and Sierra Leone at various timepoints during 2014–16. We generated a parsimonious model (viral load, age, temperature, bleeding, jaundice, dyspnea, dysphagia, and time-to-presentation) and several fallback models for when these variables are unavailable. All were externally validated against two independent datasets and compared to further models including expert observational wellness assessments. Models were incorporated into an app highlighting the signs/symptoms with the largest contribution to prognosis. Findings The parsimonious model approached the predictive power of observational assessments by experienced clinicians (Area-Under-the-Curve, AUC = 0.70–0.79, accuracy = 0.64–0.74) and maintained its performance across subcohorts with different healthcare seeking behaviors. Age and viral load contributed > 5-fold the weighting of other features and including them in a minimal model had a similar AUC, albeit at the cost of specificity. Interpretation Clinically guided prognostic models can recapitulate clinical expertise and be useful when such expertise is unavailable. Incorporating these models into mHealth tools may facilitate their interpretation and provide informed access to comprehensive clinical guidelines. Funding Howard Hughes Medical Institute, US National Institutes of Health, Bill & Melinda Gates Foundation, International Medical Corps, UK Department for International Development, and GOAL Global.
Expert system is an intelligent system to captures the knowledge of a human expert in a specific area. It is capable to make decisions and dealing with ambiguous data. It is used to take an expert view in the absence of a human expertise. Moreover, it is not possible that everyone is expert in every field; to overcome this kind of situation, the expert system is called to handle the complex cases [7]. Due to non availability of the doctor, sometimes patient's life is in risk and lead to death due to not diagnose the disease properly as there are several diseases whose symptoms are quite similar in initial stage. Hence, the objective of this paper is to design the expert system for diagnosing the diabetes to go for early treatment.
Data Mining involves extracting meaningful information from the available data in a user understandable manner. Its role is to analyze voluminous data that is being often assembled. Using the approach of Data mining techniques various business related queries can be attended which formerly were extremely time-consuming to answer. There exist uncontrollable natural disasters that critically hampers and costs human life, environment and revenue material. Natural calamities like heavy rainfall and floods cannot be well predicted until it happens, also it’s beyond one’s power to control them. The aftereffect or destruction caused by these calamities prevails for many years. The term disaster is a result of a vulnerable condition caused by heavy rainfall, flood or storm that can have intense effect at a smaller scale such as a village or at a larger scale such as city or state. Clustering model that was developed before confronted the issue of time complexity, low processing speed and were inappropriate for huge datasets. The current research work proposes the approach of K means clustering that is a subset of ML (machine learning) techniques that are capable to process huge datasets and performs quick computation compared to rest of the clustering model. Various stages in this proposed system include Dataset Collection, Pre-processing, Feature selection, K-means clustering. Among these, the K-means clustering tool which is actually a subset of data-mining and ML approach is employed to cluster observations in the form of groups. It’s a form of unsupervised learning that rectifies the clustering problem. The results reveal that the K-means clustering tool performs clustering faster than the other existing technique.
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