scholarly journals Exploratory analysis on prediction of loan privilege for customers using random forest

2018 ◽  
Vol 7 (2.21) ◽  
pp. 339 ◽  
Author(s):  
K Ulaga Priya ◽  
S Pushpa ◽  
K Kalaivani ◽  
A Sartiha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Data Model ◽  
Model Evaluation ◽  
Banking Industry ◽  
Performance Parameters ◽  
Training Set ◽  
Test Set ◽  
Learning Technique ◽  
Analytical Processing

In Banking Industry loan Processing is a tedious task in identifying the default customers. Manual prediction of default customers might turn into a bad loan in future. Banks possess huge volume of behavioral data from which they are unable to make a judgement about prediction of loan defaulters. Modern techniques like Machine Learning will help to do analytical processing using Supervised Learning and Unsupervised Learning Technique. A data model for predicting default customers using Random forest Technique has been proposed. Data model Evaluation is done on training set and based on the performance parameters final prediction is done on the Test set. This is an evident that Random Forest technique will help the bank to predict the loan Defaulters with utmost accuracy.  

Abstract 349: Prognostication for Out-of-Hospital Cardiogenic Cardiac Arrest Patients Using Advanced Machine Learning Technique

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Tomohisa Seki ◽  
Tomoyoshi Tamura ◽  
Masaru Suzuki
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Study Data ◽  
Machine Learning Techniques ◽  
Operating Characteristics ◽  
Training Set ◽  
Machine Learning Technique ◽  
Test Set ◽  
Medical Institutions ◽  
Learning Technique

Introduction and Objective: Early prognostication for cardiogenic out-of-hospital cardiac arrest (OHCA) patients remain challenging. Recently, advanced machine learning techniques have been employed for clinical diagnosis and prognostication for various conditions. Therefore, in this study, we attempted to establish a prognostication model for cardiogenic OHCA using an advanced machine learning technique. Methods and Results: Data of a prospective multi-center cohort study of OHCA patients transported by an ambulance to 67 medical institutions in Kanto area of Japan between January 2012 and March 2013 was used in this study. Data for cardiogenic OHCA patients aged ≥18 years were retrieved and patients were grouped according to the time of calls for ambulances (training set: between January 1, 2012 and December 12, 2012; test set: between January 1, 2013 and March 31, 2013). From among 421 variables observed during the period between calls for ambulances and initial in-hospital treatments of cardiogenic OHCA, 38 prehospital factors or 56 prehospital factors and initial in-hospital factors were used for prognostication, respectively. Prognostication models for 1-year survival were established with random forest method, an advanced machine learning method that aggregates a series of decision trees for classification and regression. After 10-fold internal cross validation in the training set, prognostication models were validated using test set. Area under the receiver operating characteristics curve (AUC) was used to evaluate the prediction performance of models. Prognostication models trained with 38 variables or 56 variables for 1-year survival showed AUC values of 0.93±0.01 and 0.95±0.01, respectively. Conclusions: Prognostication models trained with advanced machine learning technique showed favorable prediction capability for 1-year survival of cardiogenic OHCA. These results indicate that an advanced machine learning technique can be applicable to establish early prognostication model for cardiogenic OHCA.

scholarly journals Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jong Ho Kim ◽  
Haewon Kim ◽  
Ji Su Jang ◽  
Sung Mi Hwang ◽  
So Young Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Confidence Interval ◽  
Neck Circumference ◽  
Difficult Laryngoscopy ◽  
Gradient Boosting ◽  
Test Set ◽  
Equal Distribution ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Abstract Background Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

scholarly journals Predicting Increased Blood Pressure Using Machine Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hudson Fernandes Golino ◽  
Liliany Souza de Brito Amaral ◽  
Stenio Fernando Pimentel Duarte ◽  
Cristiano Mauro Assis Gomes ◽  
Telma de Jesus Soares ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Predictive Power ◽  
Classification Tree ◽  
Training Group ◽  
Test Sample ◽  
Hip Circumference ◽  
Training Set ◽  
Learning Technique ◽  
Tree Data

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudoR2(.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudoR2(.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

scholarly journals Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Peter Appiahene ◽  
Yaw Marfo Missah ◽  
Ussiph Najim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Banking Sector ◽  
Banking Industry ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
And Performance

The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.

scholarly journals Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 332
Author(s):  
Ernest Kwame Ampomah ◽  
Zhiguang Qin ◽  
Gabriel Nyame
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Superior Performance ◽  
Operating Characteristics ◽  
Training Set ◽  
Data Set ◽  
Test Set ◽  
Ensemble Machine Learning ◽  
Better Than

Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.

scholarly journals Mode Choice Prediction using Machine Learning Technique for A Door-to-Door Journey in Kuantan City

Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 73-78
Author(s):  
Nur Fahriza Mohd Ali ◽  
Ahmad Farhan Mohd Sadullah ◽  
Anwar P.P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman ◽  
Rabiu Muazu Musa
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Mode Choice ◽  
Learning Models ◽  
Machine Learning Technique ◽  
Travel Mode Choice ◽  
Testing Data ◽  
Learning Technique ◽  
The City ◽  
Machine Learning Models

A door-to-door journey in a public transportation system is a notable concept that is practically being promoted among users to consider public transport as an important alternative. The door-to-door journey will integrate the travel segments starting from home to destination, including all visible amenities. Users’ preferences on the time travel of these key segments are necessary to be understood. In this case, Machine Learning technique has been seen as a robust computational advancement to forecast their travel mode choice. However, the most convenient model as the best predictor is still questionable. To address this issue, we employed some pre-eminent machine learning models, specifically Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), k-Nearest Neighbor (kNN) as well as Support Vector Machine (SVM), to compare their travel mode choice prediction performance of users in the city of Kuantan. The data collection was conducted in Kuantan City via Revealed/Stated Preferences (RPSP) Survey between 8:00 AM to 5:00 PM on weekdays. The data collected was split into a ratio of 80:20 for training and testing before evaluating them between the aforesaid models. The results depicted that the Random Forest could provide satisfactory classification accuracies for both training and testing data up to 68.3% and 61.3%, respectively, compared to the other evaluated machine learning models. In summary, Random Forest provides a good result in the training and testing data and is considered as the best predictor in this research to forecast users’ mode choice in the city of Kuantan.

scholarly journals An application of machine learning to assist medication order review by pharmacists in a health care center

2019 ◽  
Author(s):  
Maxime Thibault ◽  
Denis Lebel
Keyword(s):  
Machine Learning ◽  
Neonatal Intensive Care ◽  
Care Center ◽  
Variable Order ◽  
Medication Order ◽  
Training Set ◽  
Test Set ◽  
Health Care Center ◽  
Clinical Benefits ◽  
Fold Cross Validation

AbstractThe objective of this study was to determine if it is feasible to use machine learning to evaluate how a medication order is contextually appropriate for a patient, in order to assist order review by pharmacists. A neural network was constructed using as input the sequence of word2vec embeddings of the 30 previous orders, as well as the currently active medications, pharmacological classes and ordering department, to predict the next order. The model was trained with data from 2013 to 2017, optimized using 5-fold cross-validation, and tested on orders from 2018. A survey was developed to obtain pharmacist ratings on a sample of 20 orders, which were compared with predictions. The training set included 1 022 272 orders. The test set included 95 310 orders. Baseline training set top 1, top 10 and top 30 accuracy using a dummy classifier were respectively 4.5%, 23.6% and 44.1%. Final test set accuracies were, respectively, 44.4%, 69.9% and 80.4%. Populations in which the model performed the best were obstetrics and gynecology patients and newborn babies (either in or out of neonatal intensive care). Pharmacists agreed poorly on their ratings of sampled orders with a Fleiss kappa of 0.283. The breakdown of metrics by population showed better performance in patients following less variable order patterns, indicating potential usefulness in triaging routine orders to less extensive pharmacist review. We conclude that machine learning has potential for helping pharmacists review medication orders. Future studies should aim at evaluating the clinical benefits of using such a model in practice.

scholarly journals A Line Loss Management Method Based on Improved Random Forest Algorithm in Distributed Generation System

2021 ◽  
Author(s):  
Wang Zongbao
Keyword(s):  
Power Generation ◽  
Random Forest ◽  
Random Forest Algorithm ◽  
Training Set ◽  
Test Set ◽  
Distributed Power ◽  
Distributed Power Generation ◽  
Feature Importance ◽  
Feature Relevance ◽  
Line Loss

The distributed power generation in Gansu Province is dominated by wind power and photovoltaic power. Most of these distributed power plants are located in underdeveloped areas. Due to the weak local consumption capacity, the distributed electricity is mainly sent and consumed outside. A key indicator that affects ultra-long-distance power transmission is line loss. This is an important indicator of the economic operation of the power system, and it also comprehensively reflects the planning, design, production and operation level of power companies. However, most of the current research on line loss is focused on ultra-high voltage (≧110 KV), and there is less involved in distributed power generation lines below 110 KV. In this study, 35 kV and 110 kV lines are taken as examples, combined with existing weather, equipment, operation, power outages and other data, we summarize and integrate an analysis table of line loss impact factors. Secondly, from the perspective of feature relevance and feature importance, we analyze the factors that affect line loss, and obtain data with higher feature relevance and feature importance ranking. In the experiment, these two factors are determined as the final line loss influence factor. Then, based on the conclusion of the line loss influencing factor, the optimized random forest regression algorithm is used to construct the line loss prediction model. The prediction verification results show that the training set error is 0.021 and the test set error is 0.026. The prediction error of the training set and test set is only 0.005. The experimental results show that the optimized random forest algorithm can indeed analyze the line loss of 35 kV and 110 kV lines well, and can also explain the performance of 110-EaR1120 reasonably.

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