Evaluation of prediction models for the malaria incidence in Marodijeh Region, Somaliland

2021 ◽  
Author(s):  
Jama Mohamed ◽  
Ahmed Ismail Mohamed ◽  
Eid Ibrahim Daud
Keyword(s):  
Prediction Models ◽  
Malaria Incidence

scholarly journals Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Meron Mehari Kifle ◽  
Tsega Tekeste Teklemariam ◽  
Adam Mengesteab Teweldeberhan ◽  
Eyasu Habte Tesfamariam ◽  
Amanuel Kidane Andegiorgish ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Prediction Models ◽  
Malaria Incidence ◽  
Malaria Risk ◽  
Information Criteria ◽  
Low Risk ◽  
Rainfall Data ◽  
Moderate Risk ◽  
Acceptable Accuracy

Background. Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. The development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics. Objectives. To construct a national malaria stratification map, develop prediction models and forecast monthly malaria incidences based on rainfall data. Methods. Using monthly malaria incidence data from 2012 to 2016, the district level malaria stratification was constructed by nonhierarchical clustering. Cluster validity was examined by the maximum absolute coordinate change and analysis of variance (ANOVA) with a conservative post hoc test (Bonferroni) as the multiple comparison test. Autocorrelation and cross-correlation analyses were performed to detect the autocorrelation of malaria incidence and the lagged effect of rainfall on malaria incidence. The effect of rainfall on malaria incidence was assessed using seasonal autoregressive integrated moving average (SARIMA) models. Ljung–Box statistics for model diagnosis and stationary R-squared and Normalized Bayesian Information Criteria for model fit were used. Model validity was assessed by analyzing the observed and predicted incidences using the spearman correlation coefficient and paired samples t-test. Results. A four cluster map (high risk, moderate risk, low risk, and very low risk) was the most valid stratification system for the reported malaria incidence in Eritrea. Monthly incidences were influenced by incidence rates in the previous months. Monthly incidence of malaria in the constructed clusters was associated with 1, 2, 3, and 4 lagged months of rainfall. The constructed models had acceptable accuracy as 73.1%, 46.3%, 53.4%, and 50.7% of the variance in malaria transmission were explained by rainfall in the high-risk, moderate-risk, low-risk, and very low-risk clusters, respectively. Conclusion. Change in rainfall patterns affect malaria incidence in Eritrea. Using routine malaria case reports and rainfall data, malaria incidences can be forecasted with acceptable accuracy. Further research should consider a village or health facility level modeling of malaria incidence by including other climatic factors like temperature and relative humidity.

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

The Role of Revascularisation in Patients Undergoing Non-cardiac Surgery

2010 ◽  
Vol 5 (1) ◽  
pp. 104
Author(s):  
Daniel S Menees ◽  
Eric R Bates ◽  
◽  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Models ◽  
Cardiovascular Complications ◽  
Weight Of Evidence ◽  
Beta Blockade ◽  
Cardiac Morbidity ◽  
Increased Risk ◽  
Reduction Strategies ◽  
Artery Disease ◽  
Risk Reduction Strategies

Coronary artery disease (CAD) affects millions of US citizens. As the population ages, an increasing number of people with CAD are undergoing non-cardiac surgery and face significant peri-operative cardiac morbidity and mortality. Risk-prediction models can be used to help identify those patients at increased risk of peri-operative cardiovascular complications. Risk-reduction strategies utilising pharmacotherapy with beta blockade and statins have shown the most promise. Importantly, the benefit of prophylactic coronary revascularisation has not been demonstrated. The weight of evidence suggests reserving either percutaneous or surgical revascularisation in the pre-operative setting for those patients who would otherwise meet independent revascularisation criteria.

Enterprise Performance Analysis of the Selected Service Sector by Applying Modern Methods with an Emphasis on the Creation and Application of the Modified Creditworthy Model (MCWM)

2018 ◽  
Vol 9 (17) ◽  
Author(s):  
Erika Onuferová ◽  
Veronika Čabinová
Keyword(s):  
Service Sector ◽  
Prediction Models ◽  
Economic Value ◽  
Value Added ◽  
Financial Indicators ◽  
Tourism Sector ◽  
Future Success ◽  
Financial Growth ◽  
Net Assets ◽  
Third Dimension

The aim of presented paper was to create and subsequently apply the Modified 3D Creditworthy Model (MCWM) of performance reflecting sectoral characteristics and financial specificities of the selected sample of Slovak tour operators over the years 2013 – 2017. The intention of this research study was to implement the key financial indicators and appropriate prediction models into both dimensions of the traditional 2D Creditworthy Model of performance and to supplement its third dimension applying the selected modern assessment methods – the Economic Value Added and the Return On Net Assets as we consider them to be one of the most important indicators of future success and company's financial growth. This modification will help to better identify the current financial position of tour operators and more accurately identify causes that hinder the development of financial performance of the selected sample of enterprises. However, after adjusting the upper and lower quartile averages of a particular industry, this methodology is applicable in the wider context of enterprises, not only those operating in the tourism sector.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

Sign in / Sign up

Export Citation Format

Share Document