Random Forest and Concept of Decision Tree Model

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.

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

Infotech: Journal of Technology Information ◽

10.37365/jti.v6i1.61 ◽

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.

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Spatiotemporal Prediction of the Occurrence of Vancomycin-resistant Enterococcus

10.21203/rs.3.rs-860519/v1 ◽

2021 ◽

Author(s):

J. M. van Niekerk ◽

M. Lokate ◽

L. M. A. Braakman-Jansen ◽

J. E. W. C. van Gemert-Pijnen ◽

A. Stein

Keyword(s):

Random Forest ◽

Decision Tree ◽

Antibiotic Use ◽

Random Forest Model ◽

Decision Tree Model ◽

Hospital Ward ◽

Centrality Measures ◽

Tree Model ◽

Forest Model ◽

Ward Level

Abstract Background: Vancomycin-resistant enterococci (VRE) is the cause of severe patient health and monetary burdens. Antibiotic use is a confounding effect to predict VRE in patients, but the antibiotic use of patients who may have frequented the same ward as the patient in question is often neglected. This study investigated how the occurrence and spread of VRE can be explained by patient movements between hospital wards and their antibiotic use.Methods: Intrahospital patient movements, antibiotic use and PCR screening data were used from a hospital in the Netherlands. The PageRank algorithm was used to calculate two daily centrality measures based on the spatiotemporal graph to summarise the flow of patients and antibiotics at the ward level. A decision tree model was used to determine a simple set of rules to estimate the daily probability of VRE occurrence for each hospital ward. The model performance was improved using a random forest model and compared using 30% test sample.Results: Centrality covariates summarising the flow of patients and their antibiotic use between hospital wards can be used to predict the daily occurrence of VRE at the hospital ward level. The decision tree model produced a simple set of rules that can be used to determine the daily probability of VRE occurrence for each hospital ward. An acceptable area under the ROC curve (AUC) of 0.755 was achieved using the decision tree model and an excellent AUC of 0.883 by the random forest model on the test set. These results confirms that the random forest model performs better than a single decision tree for all levels of model sensitivity and specificity on data not used to estimate the models.Conclusion: This study showed how the movements of patients inside hospitals and their use of antibiotics could predict VRE occurrence at the ward level. Two daily centrality measures were proposed to summarise the flow of patients and antibiotics at the ward level. An early warning system for VRE can be developed to test and further develop infection prevention plans and outbreak strategies using these results.

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Weighted Hybrid Decision Tree Model for Random Forest Classifier

Journal of The Institution of Engineers (India) Series B ◽

10.1007/s40031-014-0176-y ◽

2015 ◽

Vol 97 (2) ◽

pp. 209-217 ◽

Cited By ~ 4

Author(s):

Vrushali Y. Kulkarni ◽

Pradeep K. Sinha ◽

Manisha C. Petare

Keyword(s):

Random Forest ◽

Decision Tree ◽

Random Forest Classifier ◽

Decision Tree Model ◽

Tree Model

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A novel enhanced decision tree model for detecting chronic kidney disease

Network Modeling Analysis in Health Informatics and Bioinformatics ◽

10.1007/s13721-021-00302-w ◽

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

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

Diagnostics ◽

10.3390/diagnostics11061094 ◽

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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Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

International Journal of Endocrinology ◽

10.1155/2017/3894870 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Zhong Xin ◽

Lin Hua ◽

Xu-Hong Wang ◽

Dong Zhao ◽

Cai-Guo Yu ◽

...

Keyword(s):

Decision Tree ◽

Predictive Value ◽

Early Stage ◽

Current Model ◽

External Validation ◽

Area Under The Curve ◽

Decision Tree Model ◽

Tree Model ◽

Chinese Adult ◽

Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

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