• Food Science of Animal Resources
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• v.30 no.3
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• pp.487-494
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• 2010

Cooked pork can be easily contaminated with Staphylococcus aureus during carriage and serving after cooking. This study was performed to develop growth prediction models of S. aureus to assure the safety of cooked pork. The Baranyi and Gompertz primary predictive models were compared. These growth models for S. aureus in cooked pork were developed at storage temperatures of 5, 15, and $25^{\circ}C$. The specific growth rate (SGR) and lag time (LT) values were calculated. The Baranyi model, which displayed a $R^2$ of 0.98 and root mean square error (RMSE) of 0.27, was more compatible than the Gompertz model, which displayed 0.84 in both $R^2$ and RMSE. The Baranyi model was used to develop a response surface secondary model to indicate changes of LT and SGR values according to storage temperature. The compatibility of the developed model was confirmed by calculating $R^2$, $B_f$, $A_f$, and RMSE values as statistic parameters. At 5, 15 and $25^{\circ}C$, $R^2$ was 0.88, 0.99 and 0.99; RMSE was 0.11, 0.24 and 0.10; $B_f$ was 1.12, 1.02 and 1.03; and $A_f$ was 1.17, 1.03 and 1.03, respectively. The developed predictive growth model is suitable to predict the growth of S. aureus in cooked pork, and so has potential in the microbial risk assessment as an input value or model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Journal of Preventive Veterinary Medicine
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• v.42 no.4
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• pp.143-147
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• 2018

The present study aimed to develop growth prediction models of Listeria monocytogenes in processed meat products, such as mixed pressed hams, to perform accurate microbial risk assessments. Considering cold storage temperatures and the amount of time in the stages of consumption after opening, the growth of L. monocytogenes was determined as a function of temperature at 0, 5, 10, and $15^{\circ}C$, and time at 0, 1, 3, 6, 8, 10, 15, 20, 25, and 30 days. Based on the results of these measurements, a Baranyi model using the primary model was developed. The input parameters of the Baranyi equation in the variable temperature for polynomial regression as a secondary model were developed: $SGR=0.1715+0.0199T+0.0012T^2$, $LT=5.5730-0.3215T+0.0051T^2$ with $R^2$ values 0.9972 and 0.9772, respectively. The RMSE (Root mean squared error), $B_f$ (bias factor), and $A_f$ (accuracy factor) on the growth prediction model were determined to be 0.30, 0.72, and 1.50 in SGR (specific growth rate), and 0.10, 0.84, and 1.35 in LT (lag time), respectively. Therefore, the model developed in this study can be used to determine microorganism growth in the stages of consumption of mixed pressed hams and has potential in microbial risk assessments (MRAs).

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.368-378
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• 2021

Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required. Materials and Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set. Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%. Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.159-166
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• 2022

Background: A quantitative structure-activity relationship (QSAR) model was adopted in the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH, EU) regulations as well as the Act on Registration, Evaluation, etc. of Chemicals (AREC, Republic of Korea). It has been previously used in the registration of chemicals. Objectives: In this study, we investigated the correlation between the predicted data provided by three prediction programs using a QSAR model and actual experimental results (acute fish, daphnia magna toxicity). Through this approach, we aimed to effectively conjecture on the performance and determine the most applicable programs when designating toxic substances through the AREC. Methods: Chemicals that had been registered and evaluated in the Toxic Chemicals Control Act (TCCA, Republic of Korea) were selected for this study. Two prediction programs developed and operated by the U.S. EPA - the Ecological Structure-Activity Relationship (ECOSAR) and Toxicity Estimation Software Tool (T.E.S.T.) models - were utilized along with the TOPKAT (Toxicity Prediction by Komputer Assisted Technology) commercial program. The applicability of these three programs was evaluated according to three parameters: accuracy, sensitivity, and specificity. Results: The prediction analysis on fish and daphnia magna in the three programs showed that the TOPKAT program had better sensitivity than the others. Conclusions: Although the predictive performance of the TOPKAT program when using a single predictive program was found to perform well in toxic substance designation, using a single program involves many restrictions. It is necessary to validate the reliability of predictions by utilizing multiple methods when applying the prediction program to the regulation of chemicals.

• Journal of Food Hygiene and Safety
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• v.16 no.2
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• pp.103-110
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• 2001

Predictive food microbiology(PFM) is an emerging area of food microbiology since the later 1980’s. It does apply mathematical models to predict the responses of microorganism to specified environmental variables. Although, at present, PFM models do not completely developed, models can provide very useful information for microbiological responses in HACCP(Hazard Analysis Critical Control Point) system and Risk Assessment. This study illustrates the possible use of PFM models(PMP: Pathogen Modeling Program win5.1) with milk in several elements in the HACCP system, such as conduction of hazard analysis and determination of CCP(Critical Control Points) and CL(Critical Limits). The factors likely to affect the growth of the pathogens in milk involved storage fixed factors were pH 6.7, Aw 0.993 and NaCl 1.3%. PMPwin5.1 calculated generation time, lag phase duration, time to level of infective dose for pathogens across a range of storage (Critical Control Points) and CL(Critical Limits). The factors likely to affect the growth of the pathogens in milk involved storage temperature, pH, Aw and NaCl content. The factors likely to affect the growth of the pathogens in milk involved storage temperature, pH, Aw and NaCl content. The variable factor was storage temperature at the range of 4~15$^{\circ}C$ and the fixed factors were pH 6.7, Aw 0.993 and NaC 1.3%. PMPwin5.1 calculated generation time, lag phase duration, time to level of infective dose for pathogens across a range of storage temperature.

• The Journal of Bigdata
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• v.6 no.2
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• pp.161-168
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• 2021

In this study, we propose a cardiovascular disease prediction model using machine learning. First, a multidimensional analysis of various differences between the two groups is performed and the results are visualized. In particular, we propose a predictive model using cost-sensitive learning that can improve the sensitivity for cases where there is a high class imbalance between the normal and patient groups, such as diseases. In this study, a predictive model is developed using CART and XGBoost, which are representative machine learning technologies, and prediction and performance are compared for cardiovascular disease patient data. According to the study results, CART showed higher accuracy and specificity than XGBoost, and the accuracy was about 70% to 74%.

• Fisheries and Aquatic Sciences
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• v.24 no.2
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• pp.78-88
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• 2021

The annual consumption of fishery products, particularly sea squirt (Halocynthia roretzi), per person has steadily increased in South Korea. However, the quantitative risk of Vibrio parahaemolyticus following intake of sea squirt has not been analyzed. This study focuses on quantitative predictions of the probability of consuming sea squirt and getting of V. parahaemolyticus foodborne illness. The prevalence of V. parahaemolyticus in sea squirt was evaluated, and the time spent by sea squirt in transportation vehicles, market displays, and home refrigerators, in addition to the temperature of each of these, were recorded. The data were fitted to the @RISK program to obtain a probability distribution. Predictive models were developed to determine the fate of V. parahaemolyticus under distribution conditions. A simulation model was prepared based on experimental data, and a dose-response model for V. parahaemolyticus was prepared using data from literature to estimate infection risk. V. parahaemolyticus contamination was detected in 6 of 35 (17.1%) sea squirt samples. The daily consumption quantity of sea squirt was 62.14 g per person, and the consumption frequency was 0.28%. The average probability of V. parahaemolyticus foodborne illness following sea squirt consumption per person per day was 4.03 × 10^-9. The objective of this study was to evaluate the risk of foodborne illness caused by Vibrio parahaemolyticus following sea squirt consumption in South Korea.

• Health Policy and Management
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• v.18 no.4
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• pp.49-65
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• 2008

The single-item question of self-assessed health status has consistently been reported to be associated with mortality in some developed countries, even after controlling for a wide range of health measurements and known risk factors for. mortality. It was intended in this study to find out whether or not such a relationship would also be valid in Korea. This study examined the effect of point of reference year on. the, predictive validity of self-assessed health for mortality in 6-year follow-up period. we need to test the validity of the self-assessed health, as an indicator for assessing health status using Cox's proportional hazard model. For the analysis, we used the data from the 2nd (1999) to the 7th survey of "Korean Labor and Income Panel Study," and assessed relative risk of death based on subjective health state by tracing 11,366 people who replied to the question of self-assessed health state in the 2nd year. According to the result, those who reported poor self assessed health state in the 2nd year showed a relatively high death rate, and their relative risk of death was significantly higher. Such a relationship was accentuated if the predictive value of the 2nd survey result would be replaced by the average of the cumulative data on the past six years. Thus, it can be concluded that self-assessed health state is valid as an index for assessing Korean people's health status.

• Journal of Korean Academy of Nursing
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• v.54 no.2
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• pp.193-210
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• 2024

Purpose: The objective of this study was to develop a predictive model for the sexual experiences of adolescents using the random forest method and to identify the "variable importance." Methods: The study utilized data from the 2019 to 2021 Korea Youth Risk Behavior Web-based Survey, which included 86,595 man and 80,504 woman participants. The number of independent variables stood at 44. SPSS was used to conduct Rao-Scott χ² tests and complex sample t-tests. Modeling was performed using the random forest algorithm in Python. Performance evaluation of each model included assessments of precision, recall, F1-score, receiver operating characteristics curve, and area under the curve calculations derived from the confusion matrix. Results: The prevalence of sexual experiences initially decreased during the COVID-19 pandemic, but later increased. "Variable importance" for predicting sexual experiences, ranked in the top six, included week and weekday sedentary time and internet usage time, followed by ease of cigarette purchase, age at first alcohol consumption, smoking initiation, breakfast consumption, and difficulty purchasing alcohol. Conclusion: Education and support programs for promoting adolescent sexual health, based on the top-ranking important variables, should be integrated with health behavior intervention programs addressing internet usage, smoking, and alcohol consumption. We recommend active utilization of the random forest analysis method to develop high-performance predictive models for effective disease prevention, treatment, and nursing care.