Data Mining Techniques for Disease Risk Prediction Model: A Systematic Literature Review

2018 ◽  
pp. 40-46 ◽  
Author(s):  
Wan Muhamad Taufik Wan Ahmad ◽  
Nur Laila Ab Ghani ◽  
Sulfeeza Mohd Drus
Keyword(s):  
Data Mining ◽  
Literature Review ◽  
Prediction Model ◽  
Risk Prediction ◽  
Systematic Literature Review ◽  
Disease Risk ◽  
Risk Prediction Model ◽  
Data Mining Techniques

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

scholarly journals P161 Crohn's disease risk prediction model appropriately stratifies patients' risk for developing disease related complications

2017 ◽  
Vol 11 (suppl_1) ◽  
pp. S159-S159 ◽  
Author(s):  
K.D. Thompson ◽  
L.S. Siegel ◽  
T. MacKenzie ◽  
M.C. Dubinsky ◽  
C.A. Siegel
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Disease Risk ◽  
Risk Prediction Model

scholarly journals Metabolic syndrome in Xinjiang Kazakhs and construction of a risk prediction model for cardiovascular disease risk

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0202665 ◽  
Author(s):  
Lei Mao ◽  
Jia He ◽  
Xiang Gao ◽  
Heng Guo ◽  
Kui Wang ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Metabolic Syndrome ◽  
Prediction Model ◽  
Risk Prediction ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Risk Prediction Model

scholarly journals Attributable risk of all-cause mortality in hypertensive adults based on disease risk prediction model: A Chinese cohort study

2020 ◽  
Vol 13 (9) ◽  
pp. 1290-1296
Author(s):  
Weiwei Sun ◽  
Yanhong Huo ◽  
Qingqing Liu ◽  
Andrew C. Ahn ◽  
Jingwei Zhou ◽  
...  
Keyword(s):  
Cohort Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Disease Risk ◽  
Attributable Risk ◽  
Risk Prediction Model ◽  
All Cause Mortality ◽  
Chinese Cohort

Data Mining Techniques of Complaint Reports for E-government: A Systematic Literature Review

2020 ◽  
Author(s):  
Evaristus Didik Madyatmadja ◽  
Debri Pristinella ◽  
Martinus Damitutsa Kurnia Dewa ◽  
Hendro Nindito ◽  
Cristofer Wijaya
Keyword(s):  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Mining Techniques
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