scholarly journals Prediction models for burden of caregivers applying data mining techniques

2017 ◽  
Vol 2 (5) ◽  
pp. 3-3
Author(s):  
Jose Gutierrez ◽  
Debbie Schauer ◽  
Maria Patrao ◽  
Sunmoo Yoon
Keyword(s):  
Data Mining ◽  
Prediction Models ◽  
Data Mining Techniques

scholarly journals Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques

2020 ◽  
Vol 12 (23) ◽  
pp. 9790
Author(s):  
Sanghoon Lee ◽  
Keunho Choi ◽  
Donghee Yoo
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Prediction Model ◽  
Internal Control ◽  
Prediction Models ◽  
Influential Factors ◽  
Financial Information ◽  
Data Mining Techniques ◽  
Feasibility Assessment ◽  
Using Data

The government makes great efforts to maintain the soundness of policy funds raised by the national budget and lent to corporate. In general, previous research on the prediction of company insolvency has dealt with large and listed companies using financial information with conventional statistical techniques. However, small- and medium-sized enterprises (SMEs) do not have to undergo mandatory external audits, and the quality of accounting information is low due to weak internal control. To overcome this problem, we developed an insolvency prediction model for SMEs using data mining techniques and technological feasibility assessment information as non-financial information. We divided the dataset into two types of data based on three years of corporate age. The synthetic minority over-sampling technique (SMOTE) was used to solve the data imbalance that occurred at this time. Six insolvency prediction models were created using logistic regression, a decision tree, an artificial neural network, and an ensemble (i.e., boosting) of each algorithm. By applying a boosted decision tree, the best accuracies of 69.1% and 82.7% were derived, and by applying a decision tree, nine and seven influential factors affected the insolvency of SMEs established for fewer than three years and more than three years, respectively. In addition, we derived several insolvency rules for the two types of SMEs from the decision tree-based prediction model and proposed ways to enhance the health of loans given to potentially insolvent companies using these derived rules. The results of this study show that it is possible to predict SMEs’ insolvency using data mining techniques with technological feasibility assessment information and find meaningful rules related to insolvency.

scholarly journals Cardiovascular Disease Prediction System Using Extra Trees Classifier

2019 ◽  
Author(s):  
Rahman Shafique ◽  
Arif Mehmood ◽  
Saleem ullah ◽  
Gyu Sang Choi
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Health Care ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Support Vector ◽  
Prediction System ◽  
Classification Techniques ◽  
Data Mining Techniques ◽  
Tree Classifier

Abstract Heart Disease as cardiovascular disease is the leading cause of death for both men and women. It is the major cause of morbidity and mortality in present society. Therefore, researchers are working to help health care professionals in diagnosing process by using data mining techniques. Although the health care industry is richer in the database this data is not properly mined in order to discover hidden patterns and can able to make decisions based on these patterns. The major goal of this learning refers the extraction of hidden layers by applying numerous data mining techniques that probably give remarkable results in order to ensure the presence of cardiovascular disease among peoples. Data mining classification techniques are used to discover these patterns for research in medical industry. The dataset containing 13 attributes has analyzed for prediction system. The dataset contains some commonly used medical terms like blood pressure, cholesterol level, chest pain and 11 other attributes used to predict cardiovascular disease. The most common and effective classification techniques that are used in mining process are Verdict Tree commonly known as Decision Tree, Extra Trees Classifier, Random Forest, Support Vector Machine, Naive Bays and Logistic Regression has analyzed in this paper. Diagnosing and controlling ratio of deaths from cardiovascular disease Extra classifier trees consider is the best approach. We evaluate these prediction models by using evaluation parameters which are Accuracy, Precision, Recall, and F1-score. As per our experimental results shows accuracy of Extra trees classifier, Logistic Model tree classifier, support vector machine, and naive bays classifiers are 90%, 88%, 87%, 86% respectively. So as per our experiment analysis Extra Tree classifier with highest accuracy considered best approach for predication cardiovascular disease.

scholarly journals Developing an ANN Based Streamflow Forecast Model Utilizing Data-Mining Techniques to Improve Reservoir Streamflow Prediction Accuracy: A Case Study

2018 ◽  
Vol 4 (5) ◽  
pp. 1135 ◽  
Author(s):  
Hamed Zamanisabzi ◽  
James Phillip King ◽  
Naci Dilekli ◽  
Bahareh Shoghli ◽  
Shalamu Abudu
Keyword(s):  
Data Mining ◽  
Correlation Analysis ◽  
Prediction Models ◽  
Forecast Model ◽  
Physical Parameters ◽  
Streamflow Prediction ◽  
Data Mining Techniques ◽  
Monthly Streamflow ◽  
Data Extracting

This study illustrates the benefits of data pre-processing through supervised data-mining techniques and utilizing those processed data in an artificial neural networks (ANNs) for streamflow prediction. Two major categories of physical parameters such as snowpack data and time-dependent trend indices were utilized as predictors of streamflow values.  Correlation analysis of different models indicate that, for the period of January to June, using fewer predictors led to simpler modeling with equivalent accuracy on daily prediction models. This did not hold in all periods. For monthly prediction models, accuracy was improved compared to earlier works done to predict monthly streamflow for the same case of Elephant Butte Reservoir (EB), NM. Overall, superior prediction performance was achieved by utilizing data-mining techniques for pre-processing historical data, extracting the most effective predictors, correlation analysis, extracting and utilizing combined climate variability indices, physical indices, and employing several developed ANNs for different prediction periods of the year.

scholarly journals Comparative Analysis for Heart Disease Prediction

2017 ◽  
Vol 1 (4-2) ◽  
pp. 227
Author(s):  
Sundas Naqeeb Khan ◽  
Nazri Mohd Nawi ◽  
Asim Shahzad ◽  
Arif Ullah ◽  
Muhammad Faheem Mushtaq ◽  
...  
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Efficient Prediction ◽  
Causes Of Deaths

Today, heart diseases have become one of the leading causes of deaths in nationwide. The best prevention for this disease is to have an early system that can predict the early symptoms which can save more life. Recently research in data mining had gained a lot of attention and had been used in different kind of applications including in medical. The use of data mining techniques can help researchers in predicting the probability of getting heart diseases among susceptible patients. Among prior studies, several researchers articulated their efforts for finding a best possible technique for heart disease prediction model. This study aims to draw a comparison among different algorithms used to predict heart diseases. The results of this paper will helps towards developing an understanding of the recent methodologies used for heart disease prediction models. This paper presents analysis results of significant data mining techniques that can be used in developing highly accurate and efficient prediction model which will help doctors in reducing the number of deaths cause by heart disease.

Audit Opinion Prediction: A Comparison of Data Mining Techniques

2020 ◽  
Author(s):  
Ali Saeedi
Keyword(s):  
Data Mining ◽  
New York ◽  
Prediction Models ◽  
Stock Exchange ◽  
Support Vector ◽  
Type I ◽  
K Nearest Neighbors ◽  
Data Mining Techniques ◽  
Financial Variables ◽  
Audit Opinions

This study compares the ability of four data mining techniques in the prediction of audit opinions on companies' financial statements. The research data consists of 37,325 firm-year observations for companies listed on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), and the NASDAQ from 2001 to 2017. The dataset consists of U.S. companies' variousfinancial and non-financial variables. This study uses Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (k-NN), and Rough Sets (RS) to develop the prediction models. While all models developed by these four techniques predict the audit opinions with relatively high accuracy, the SVM models developed by RBF kernel demonstrate the highest performance in terms of overall prediction accuracy rates and Type I and Type II errors. The results indicate that all models developed using different algorithms demonstrate their highest performance in predicting going-concern modifications, ranging from 84.2 to 100 percent.

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