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 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 A diagnostic strategy for Parkinsonian syndromes using quantitative indices of DAT SPECT and MIBG scintigraphy: an investigation using the classification and regression tree analysis

2021 ◽  
Author(s):  
Yu Iwabuchi ◽  
Masashi Kameyama ◽  
Yohji Matsusaka ◽  
Hidetoshi Narimatsu ◽  
Masahiro Hashimoto ◽  
...  
Keyword(s):  
Decision Tree ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Mibg Scintigraphy ◽  
Tree Model ◽  
Quantitative Indices ◽  
Cart Analysis ◽  
Parkinsonian Syndromes ◽  
Dat Spect ◽  
Classification And Regression

Abstract Purpose We aimed to evaluate the diagnostic performances of quantitative indices obtained from dopamine transporter (DAT) single-photon emission computed tomography (SPECT) and 123I-metaiodobenzylguanidine (MIBG) scintigraphy for Parkinsonian syndromes (PS) using the classification and regression tree (CART) analysis. Methods We retrospectively enrolled 216 patients with or without PS, including 80 without PS (NPS) and 136 with PS [90 Parkinson’s disease (PD), 21 dementia with Lewy bodies (DLB), 16 progressive supranuclear palsy (PSP), and 9 multiple system atrophy (MSA). The striatal binding ratio (SBR), putamen-to-caudate ratio (PCR), and asymmetry index (AI) were calculated using DAT SPECT. The heart-to-mediastinum uptake ratio (H/M) based on the early (H/M [Early]) and delayed (H/M [Delay]) images and cardiac washout rate (WR) were calculated from MIBG scintigraphy. The CART analysis was used to establish a diagnostic decision tree model for differentiating PS based on these quantitative indices. Results The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 87.5, 96.3, 93.3, 92.9, and 93.1 for NPS; 91.1, 78.6, 75.2, 92.5, and 83.8 for PD; 57.1, 95.9, 60.0, 95.4, and 92.1 for DLB; and 50.0, 98.0, 66.7, 96.1, and 94.4 for PSP, respectively. The PCR, WR, H/M (Delay), and SBR indices played important roles in the optimal decision tree model, and their feature importance was 0.61, 0.22, 0.11, and 0.05, respectively. Conclusion The quantitative indices showed high diagnostic performances in differentiating NPS, PD, DLB, and PSP, but not MSA. Our findings provide useful guidance on how to apply these quantitative indices in clinical practice.

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