scholarly journals Determination of Seroprevalence of Contagious Caprine Pleuropneumonia and Associated Risk Factors in Goats and Sheep Using Classification and Regression Tree

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1165
Author(s):  
Abdelfattah Selim ◽  
Ameer Megahed ◽  
Sahar Kandeel ◽  
Abdullah D. Alanazi ◽  
Hamdan I. Almohammed
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Logistic Regression ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Flock Size ◽  
Factors Associated ◽  
Contagious Caprine Pleuropneumonia ◽  
Classification And Regression ◽  
Communal Feeding

Classification and Regression Tree (CART) analysis is a potentially powerful tool for identifying risk factors associated with contagious caprine pleuropneumonia (CCPP) and the important interactions between them. Our objective was therefore to determine the seroprevalence and identify the risk factors associated with CCPP using CART data mining modeling in the most densely sheep- and goat-populated governorates. A cross-sectional study was conducted on 620 animals (390 sheep, 230 goats) distributed over four governorates in the Nile Delta of Egypt in 2019. The randomly selected sheep and goats from different geographical study areas were serologically tested for CCPP, and the animals’ information was obtained from flock men and farm owners. Six variables (geographic location, species, flock size, age, gender, and communal feeding and watering) were used for risk analysis. Multiple stepwise logistic regression and CART modeling were used for data analysis. A total of 124 (20%) serum samples were serologically positive for CCPP. The highest prevalence of CCPP was between aged animals (>4 y; 48.7%) raised in a flock size ≥200 (100%) having communal feeding and watering (28.2%). Based on logistic regression modeling (area under the curve, AUC = 0.89; 95% CI 0.86 to 0.91), communal feeding and watering showed the highest prevalence odds ratios (POR) of CCPP (POR = 3.7, 95% CI 1.9 to 7.3), followed by age (POR = 2.1, 95% CI 1.6 to 2.8) and flock size (POR = 1.1, 95% CI 1.0 to 1.2). However, higher-accuracy CART modeling (AUC = 0.92, 95% CI 0.90 to 0.95) showed that a flock size >100 animals is the most important risk factor (importance score = 8.9), followed by age >4 y (5.3) followed by communal feeding and watering (3.1). Our results strongly suggest that the CCPP is most likely to be found in animals raised in a flock size >100 animals and with age >4 y having communal feeding and watering. Additionally, sheep seem to have an important role in the CCPP epidemiology. The CART data mining modeling showed better accuracy than the traditional logistic regression.

scholarly journals Analisis Kecenderungan Keterlambatan Pembayaran Pengecekan Kapal di Pelabuhan Regional Riau

2019 ◽  
Vol 2 (2) ◽  
pp. 92-98
Author(s):  
Hespri Yomeldi ◽  
Moh Roufiq Azmy ◽  
Ryche Pranita
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Ministry Of Health ◽  
Tree Algorithm ◽  
Health Checks ◽  
Depth Study ◽  
Classification And Regression ◽  
Port Health

Ship health checks must be carried out which function to provide a sailing permit. The implementation of ship health checks is carried out in collaboration with the ministry of health and transportation. The implementation of the activity, commonly known as Port Health Quarantine Clearance (PHQC) requires time to check and the ship makes a payment check to be able to issue a sailing permit. The problem that arises in the field is that the ship delays PHQC payments and then impacts on the buildup of ships in the port, besides that officers also need longer time to process the issuance of sailing permits. This of course has an impact on other port services such as dwelling time and scheduled departures that can be delayed. In overcoming this problem, an in-depth study is needed to analyze the trend of late payment of ship health checks, what variables influence it and how treatment is done to overcome these problems. Using logistic regression and decision tree with Classification and Regression Tree algorithm , a model is then developed that determines the variables that affect the delay of the ship making PHQC payments.

scholarly journals Risk factors associated with the mortality of Acinetobacter baumannii

2020 ◽  
Vol 2 (7A) ◽  
Author(s):  
Diaa Alrahmany ◽  
Sirous Golchinheydari ◽  
Islam M. Ghazi
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Critical Care ◽  
Acinetobacter Baumannii ◽  
Regression Tree ◽  
Antimicrobial Treatment ◽  
Classification And Regression Tree ◽  
Cart Analysis ◽  
Critical Care Admission ◽  
General Wards

Background: Acinetobacter baumannii (AB) was declared an antibiotic-resistant “Priority 1 pathogen” by WHO. We sought to investigate the predisposing risk factors to this pathogen. Methods: In a retrospective study, adults who were admitted to Sohar hospital during 2016-2017 and had a positive laboratory-confirmed culture of AB were studied.We classified patients into 2 groups based on 30-day, all-cause mortality and compared the characteristics. Exploratory classification and regression tree (CART) analysis was performed to explore risk factors for mortality to include to a logistic regression model. Results: A total of 321 patients were included, age was (Mean±SD) 57.42±20.22, male gender was 180(56.07%), mortality was 140(44%). Survivors vs deceased had; length of stay 38.25±88.74 vs 51.31±79.19 (p=0.002),multi-drug resistantisolates 134(51.34%) vs 127(48.66%) p=<0.001, critical care admission 35(38.04%) vs 57(61.96%) p=<0.001, comorbidities 114(47.50%) vs 126(52.50%) p=<0.001 and history of invasive procedures 82(59.85%) vs 55(40.15%) p=0.27. Logistic regression revealed that the odds of dying increase by a factor of 1.044 for every additional year of age, 1.844 times higher for male compared to female, 4.412 times higher for patients admitted into critical care units compared to general wards, 3.138 times higher for patients admitted with a diagnosis of infection, 2.356 times higher for patients with hospital-acquired AB infection compared to community-acquired. Conclusion: Both modifiable and non-modifiable risk factors are associated with mortality and overall health status may contribute to infection outcome. Stabilization of comorbidities and effective antimicrobial treatment could be the mainstay of successful prevention.

Classification of 3G Mobile Phone Customers

2009 ◽  
pp. 2862-2870
Author(s):  
Ankur Jain ◽  
Lalit Wangikar ◽  
Martin Ahrens ◽  
Ranjan Rao ◽  
Suddha Sattwa Kundu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
The Third ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
3G Mobile Phone ◽  
Adaptive Regression Splines

In this article we discuss how we have predicted the third generation (3G) customers using logistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables standalone can be improved upon, by the use for various functions of the raw variables and dummies representing potential segments of the population

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