Development of Prediction Model of Financial Distress and Improvement of Prediction Performance Using Data Mining Techniques

2016 ◽  
Vol 18 (2) ◽  
pp. 173-198
Author(s):  
Raynghyung Kim ◽  
◽  
Donghee Yoo ◽  
Gunwoo Kim
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Financial Distress ◽  
Prediction Performance ◽  
Data Mining Techniques ◽  
Using Data

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 Paddy Yield Prediction Model Using Data Mining Techniques

NCICCNDA ◽  
2018 ◽  
Author(s):  
Insha Sirur ◽  
Karthik B ◽  
Sharath P ◽  
Mohan Kumari M ◽  
Rumana Anjum
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Using Data ◽  
Paddy Yield

Using Data Mining Techniques to Predict Obstetric Fistula in Tanzania

2021 ◽  
pp. 169-191
Author(s):  
Titus Fihavango ◽  
Mustafa Habibu Mohsini ◽  
Leonard J. Mselle
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Obstetric Fistula ◽  
Evidence Based ◽  
Patient Records ◽  
Medical Sciences ◽  
Data Mining Techniques ◽  
Improved Performance ◽  
Using Data ◽  
Accuracy Performance

DM practices in medical sciences have brought about improved performance in analysis of large and complex datasets. DM facilitates evidence-based medical hypotheses. Nowadays, health diseases, especially obstetric fistula, are increasing. CCBRT reports, approximately 3,000 women suffer from obstetric fistula annually. Since efforts to eradicate obstetric fistula have been inadequate, the researcher was motivated to employ MLA in BIO informatics to detect obstetric fistula. The purpose of this chapter was to use DM techniques to predict obstetric fistula. The datasets involving 367 patient records from January 2015 to February 2019 were collected from CCBRT. The environment was used to describe the accurate of predictive model was CV, ROC, and CM. The research was performed using six different MLA. The accuracy performance between algorithms shows that LR has better accuracies of 87.678%, precision measures of 91%, recall measures of 82%, f1-score measures of 86%, and support measures of 74%. Thus, the researcher chose to use LR as the proposed obstetric fistula prediction model.

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