scholarly journals Application of decision tree for prediction of cutaneous leishmaniasis incidence based on environmental and topographic factors in Isfahan Province, Iran

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Roghieh Ramezankhani ◽  
Nooshin Sajjadi ◽  
Roya Nezakati Esmaeilzadeh ◽  
Seyed Ali Jozi ◽  
Mohammad Reza Shirzadi
Keyword(s):  
Environmental Factors ◽  
Decision Tree ◽  
Cutaneous Leishmaniasis ◽  
Decision Tree Model ◽  
Vegetation Coverage ◽  
Tree Model ◽  
Seasonal Incidence ◽  
Isfahan Province ◽  
The Mean ◽  
The Impact

Cutaneous Leishmaniasis (CL) is a neglected tropical disease that continues to be a health problem in Iran. Nearly 350 million people are thought to be at risk. We investigated the impact of the environmental factors on CL incidence during the period 2007- 2015 in a known endemic area for this disease in Isfahan Province, Iran. After collecting data with regard to the climatic, topographic, vegetation coverage and CL cases in the study area, a decision tree model was built using the classification and regression tree algorithm. CL data for the years 2007 until 2012 were used for model construction and the data for the years 2013 until 2015 were used for testing the model. The Root Mean Square error and the correlation factor were used to evaluate the predictive performance of the decision tree model. We found that wind speeds less than 14 m/s, altitudes between 1234 and 1810 m above the mean sea level, vegetation coverage according to the normalized difference vegetation index (NDVI) less than 0.12, rainfall less than 1.6 mm and air temperatures higher than 30°C would correspond to a seasonal incidence of 163.28 per 100,000 persons, while if wind speed is less than 14 m/s, altitude less than 1,810 m and NDVI higher than 0.12, then the mean seasonal incidence of the disease would be 2.27 per 100,000 persons. Environmental factors were found to be important predictive variables for CL incidence and should be considered in surveillance and prevention programmes for CL control.

scholarly journals Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013

2021 ◽  
Vol 18 (10) ◽  
pp. 5126
Author(s):  
Pouya Gholizadeh ◽  
Ikechukwu S. Onuchukwu ◽  
Behzad Esmaeili
Keyword(s):  
Decision Tree ◽  
Occupational Safety ◽  
Decision Rules ◽  
Decision Tree Model ◽  
Body Parts ◽  
Contributing Factors ◽  
Tree Model ◽  
Statistical Probability ◽  
Electrical Burns ◽  
The Impact

This study used methodologies of descriptive and quantitative statistics to identify the contributing factors most affecting occupational accident outcomes among electrical contracting enterprises, given an accident occurred. Accident reports were collected from the Occupational Safety and Health Administration’s fatality and catastrophe database. To ensure the reliability of the data, the team manually codified more than 600 incidents through a comprehensive content analysis using injury-classification standards. Inclusive of both fatal and non-fatal injuries, the results showed that most accidents happened in nonresidential buildings, new construction, and small projects (i.e., $50,000 or less). The main source of injuries manifested in parts and materials (46%), followed by tools, instruments, and equipment (19%), and structure and surfaces (16%). The most frequent types of injuries were fractures (31%), electrocutions (27%), and electrical burns (14%); the main injured body parts were upper extremities (25%), head (23%), and body system (18%). Among non-fatal cases, falls (37%), exposure to electricity (36%), and contact with objects (19%) caused most injuries; among fatal cases, exposure to electricity was the leading cause of death (50%), followed by falls (28%) and contact with objects (19%). The analysis also investigated the impact of several accident factors on the degree of injuries and found significant effects from such factors such as project type, source of injury, cause of injury, injured part of body, nature of injury, and event type. In other words, the statistical probability of a fatal accident—given an accident occurrence—changes significantly based on the degree of these factors. The results of this study, as depicted in the proposed decision tree model, revealed that the most important factor for predicting the nature of injury (electrical or non-electrical) is: whether the source of injury is parts and materials; followed by whether the source of injury is tools, instruments, and equipment. In other words, in predicting (with a 94.31% accuracy) the nature of injury as electrical or non-electrical, whether the source of injury is parts and materials and whether the source of injury is tools, instruments, and equipment are very important. Seven decision rules were derived from the proposed decision tree model. Beyond these outcomes, the described methodology contributes to the accident-analysis body of knowledge by providing a framework for codifying data from accident reports to facilitate future analysis and modeling attempts to subsequently mitigate more injuries in other fields.

scholarly journals ПОБУДОВА Й АНАЛІЗ МОДЕЛІ ПРИЙНЯТТЯ РІШЕНЬ ПРИ ОЦІНЦІ НАСЛІДКІВ ВАКЦИНАЦІЇ ПРОТИРОТАВІРУСНОЮ ВАКЦИНОЮ В УКРАЇНІ

2012 ◽  
Author(s):  
S. O. Solovyov ◽  
I. V. Dziublyk
Keyword(s):  
Decision Tree ◽  
Markov Processes ◽  
Hospital Admissions ◽  
Decision Tree Model ◽  
Theoretical Studies ◽  
Rotavirus Vaccine ◽  
Computer Implementation ◽  
Tree Model ◽  
The Impact ◽  
Efficiency Indicators

The results of computational and theoretical studies related to assessing of efficiency indicators of immunization with rotavirus vaccine in Ukraine among children under five years are presented. The Indicators of the impact were received with computer implementation of decision tree model based on Markov processes. Under strategies of vaccination and no vaccination projected levels of morbidity, number of hospital admissions, mortality of rotavirus infection and other factors were received. It was shown that the vaccination with rotavirus vaccine will have significant medical significance in Ukraine.

scholarly journals PENERAPAN ALGORITMA KLASIFIKASI RANDOM FOREST UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT DI KOPERASI MITRA SEJAHTERA

2020 ◽  
Vol 6 (1) ◽  
pp. 7-14
Author(s):  
Achmad Udin Zailani ◽  
Nugraha Listiana Hanun
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Study Results ◽  
Credit Worthiness ◽  
The Impact ◽  
Credit Analysis

In English : Credit is the provision of money or bills which can be equalized with an agreement or deal between the bank and another parties that requires the borrower to pay off the debt after a certain period of time through interest. Before the cooperative approves the credit proposed by the debtor, the cooperative conducts a credit analysis of borrowers whether the credit application is approved or disapproved. This study objectives to predict creditworthiness by applying the Random Forest Classification Algorithm in order to provide a solution for determining the creditworthiness.This research method is absolute experimental research that leads to the impact resulting from experiments on the application of the decision tree model of the Random Forest Classification Algorithm’s approach. The study results using the Random Forest Classification Algorithm’s are able to analyze problem credit and disproblems debtors with an accuracy value of 87.88%. Besides that,. decision tree model was able to improve the accuracy in analyzing the credit worthiness of borrowers who filed. In Indonesian : Kredit adalah penyediaan uang atau tagihan yang dapat dipersamakan atas persetujuan atau kesepakatan pinjam meminjam antara bank dengan pihak lain yang mewajibkan pihak peminjam melunasi utangnya setelah jangka waktu tertentu dengan pemberian bunga. Koperasi Mitra Sejahtera menghadapi masalah pembayaran pihak peminjam atas tunggakan kredit. Penelitian ini bertujuan untuk memprediksi kelayakan kredit dengan penerapan Algoritma Klasifikasi Random Forest agar dapat memberikan solusi untuk penentuan kelayakan pemberian kredit. Metode penelitian ini adalah riset eksperimen absolut yang mengarah kepada dampak yang dihasilkan dari eksperimen atas penerapan model pohon keputusan menggunakan pendekatan Algoritma Klasifikasi Random Forest. Hasil pengujian dengan algoritma klasifikasi Random Forest mampu menganalisis kredit yang bermasalah dan yang debitur yang tidak bermasalah dengan nilai akurasi sebesar 87,88%. Di samping itu, model pohon keputusan ternyata mampu meningkatkan akurasi dalam menganalisis kelayakan kredit yang diajukan calon debitur.

scholarly journals PENERAPAN ALGORITMA KLASIFIKASI RANDOM FOREST UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT DI KOPERASI MITRA SEJAHTERA

2020 ◽  
Vol 6 (1) ◽  
pp. 7-14
Author(s):  
Achmad Udin Zailani ◽  
Nugraha Listiana Hanun
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Study Results ◽  
Credit Worthiness ◽  
The Impact ◽  
Credit Analysis

Credit is the provision of money or bills which can be equalized with an agreement or deal between the bank and another parties that requires the borrower to pay off the debt after a certain period of time through interest. Before the cooperative approves the credit proposed by the debtor, the cooperative conducts a credit analysis of borrowers whether the credit application is approved or disapproved. This study objectives to predict creditworthiness by applying the Random Forest Classification Algorithm in order to provide a solution for determining the creditworthiness.This research method is absolute experimental research that leads to the impact resulting from experiments on the application of the decision tree model of the Random Forest Classification Algorithm’s approach. The study results using the Random Forest Classification Algorithm’s are able to analyze problem credit and disproblems debtors with an accuracy value of 87.88%. Besides that,. decision tree model was able to improve the accuracy in analyzing the credit worthiness of borrowers who filed.

scholarly journals Index split decision tree and compositional deep neural network for text categorization

2017 ◽  
Vol 7 (1.1) ◽  
pp. 449
Author(s):  
N Ravikumar ◽  
Dr P. Tamil Selvan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Tree ◽  
Text Categorization ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Decision Tree Model ◽  
Tree Model ◽  
The Impact

Text categorization with machine learning algorithms generally reckons to possess horizontal set of classes. Several advanced machine learning algorithms have been designed in the past few decades. With the growing research work for text categorization, it has become important to categorize the research outcome and provide the learners with an effective machine learning method, a framework called, Hierarchical Decision Tree and Deep Neural Network (HDT-DNN).It investigates machine learning algorithms to create horizontal set of classes and it is used for classification of text. With this objective, a novel and efficient text categorization framework based on decision tree model is used in order to categorize text according to superior and subordinate level. The text to be categorized is presented in the form of a tree with parent text category being superior to all. The intermediate level represents the text that is both superior and subordinate. Then Deep Neural Network model is presented initiating compositional model, where the text has to be categorized, as a layered integration of primitives from the constructed decision tree model. The extra layers enable composition of features from lower layers, potentially modeling complex text with fewer units than a similarly carried out shallow network producing hierarchical classification. The significance of the impact of HDT-DNN framework is evaluated through empirical study. Extensive experiments are carried out and the performance of HDT-DNN framework is evaluated and compared with existing state-of-art methods using parameters such as precision, classification accuracy, classification time, with respect to varied number of features and document size.

scholarly journals Effectiveness and Cost-Effectiveness of TeleStroke Consultations to Support the Care of Stroke Patients Presenting to Regional Emergency Departments in Western Australia: An Economic Evaluation Case Study Protocol

2020 ◽  
Author(s):  
Christina Tsou ◽  
Suzanne Robinson ◽  
James Boyd ◽  
Shruthi Kamath ◽  
Justin Yeung ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Decision Tree ◽  
Western Australia ◽  
Decision Tree Model ◽  
Service Development ◽  
List Type ◽  
Tree Model ◽  
Human Research ◽  
The Impact

ABSTRACTIntroductionThe Western Australia Acute TeleStroke Programme commenced incrementally across regional Western Australia (WA) during 2016-2017. Since the introduction of the TeleStroke Programme, there has been monitoring of service outputs including regional patient access to tertiary stroke specialist advice and reperfusion treatment, however, the impact of consultation with a stroke specialist via telehealth (videoconferencing or telephone) on the effectiveness and cost-effectiveness of stroke care, and the drivers of cost-effectiveness has not been systematically evaluated.Methods and AnalysisThe aim of the case study is to examine the impact of consultation with a stroke specialist via telehealth on the effectiveness and cost-effectiveness of stroke and TIA care using a mixed methods approach. A categorical decision tree model will be constructed in collaboration with clinicians and programme managers. A before and after comparison using State-wide administrative datasets will be used to run the base model. If sample size and statistical power permits, the cases and comparators will be matched by stroke type and presence of CT scan at the initial site of presentation, age category and presenting hospital. The drivers of cost-effectiveness will be explored through stakeholder interviews. Data from the qualitative analysis will be cross-referenced with trends emerging from the quantitative dataset and used to guide the factors to be involved in sub-group and sensitivity analysis.Ethics and DisseminationEthics approval for this case study has been granted from the WACHS Human Research and Ethics Committee (RGS3076). Reciprocal approval has been granted from Curtin University Human Research Ethics Office (HRE2019-0740). Findings will be disseminated publicly through conference presentation and peer-review publications. Interim findings will be released as internal reports to inform the service development.Strengths and Limitations of This StudyComparison of the impact of stroke specialist consultation via telehealth in regional Australia in supporting the management of different stroke subtypesThe decision tree model will be constructed in collaboration with clinicians and programme administrators directly involved in the delivery of the TeleStroke ProgrammeUse of local administrative data as model inputs enables the base model to reflect the reality of the regional WA Health service deliveryCollaboration with WA Health stakeholders involved in TeleStroke Programme design and implementation to optimise utility of the case study to inform service development and expansionConversion of the functional outcome modified Rankin Scale score (mRS) to quality adjusted life years (QALY) relies on national or international averages

scholarly journals Evaluation of Legislation Adequacy in Managing Time and Quality Performance in Iraqi Construction Projects- a Bayesian Decision Tree Approach

2018 ◽  
Vol 4 (5) ◽  
pp. 993 ◽  
Author(s):  
Zainab Hassan ◽  
Amer M. Ibrahim ◽  
Hafeth Naji
Keyword(s):  
Decision Tree ◽  
Construction Projects ◽  
Construction Project ◽  
Decision Tree Model ◽  
Bayesian Decision ◽  
Tree Model ◽  
Quality Performance ◽  
High Level ◽  
The Impact ◽  
Very High

Delay and quality defects are significant problems in Iraqi construction projects. During the period from 2003-2014, legislation has been changed to enhance the performance of construction project. This change is done by modifying some clauses of legislation and adding or deleting the others. The aim of this study is to evaluate the adequacy of these changes by using questionnaire and Bayesian decision tree model. 30 projects were taken for the period from 2003-2014. Performance of construction project was assessed on one hand by conducting a questionnaire which depend on the impact of legislation clauses on the time and quality performance, while on the other hand Bayesian decision tree model was developed in which qualitative estimate of time and quality performance by using KNIME program. The results of questionnaire estimate the delay from very low to very high and quality from very low to high in Iraqi construction industry. The results of Bayesian decision tree model reveal that the high percentage of construction projects were implemented with very high delay and high level of quality. The model gives good accuracy in prediction time and quality performance about 86.7%. These results show the enhancement in the quality performance is greater than the time performance under the legislative change. The model can assist the Iraqi legislator in evaluation the impact of legislation on time and quality performance of construction project.

A novel enhanced decision tree model for detecting chronic kidney disease

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Avijit Kumar Chaudhuri ◽  
Deepankar Sinha ◽  
Dilip K. Banerjee ◽  
Anirban Das
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model

scholarly journals Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1094
Author(s):  
Michael Wong ◽  
Nikolaos Thanatsis ◽  
Federica Nardelli ◽  
Tejal Amin ◽  
Davor Jurkovic
Keyword(s):  
Decision Tree ◽  
Expectant Management ◽  
Decision Tree Model ◽  
Decision Tree Analysis ◽  
Focal Lesions ◽  
Tree Model ◽  
Normal Endometrium ◽  
Endometrial Polyps ◽  
Tree Analysis ◽  
Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

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