• Korean Journal of Artificial Intelligence
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• v.11 no.1
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• pp.1-7
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• 2023

In this paper, a study was conducted to compare the prediction model of cardiovascular disease occurrence. It is the No.1 disease that accounts for 1/3 of the world's causes of death, and it is also the No. 2 cause of death in Korea. Primary prevention is the most important factor in preventing cardiovascular diseases before they occur. Early diagnosis and treatment are also more important, as they play a role in reducing mortality and morbidity. The Results of an experiment using Azure ML, Logistic Regression showed 88.6% accuracy, Decision Tree showed 86.4% accuracy, and Support Vector Machine (SVM) showed 83.7% accuracy. In addition to the accuracy of the ROC curve, AUC is 94.5%, 93%, and 92.4%, indicating that the performance of the machine learning algorithm model is suitable, and among them, the results of applying the logistic regression algorithm model are the most accurate. Through this paper, visualization by comparing the algorithms can serve as an objective assistant for diagnosis and guide the direction of diagnosis made by doctors in the actual medical field.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• Economic and Environmental Geology
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• v.56 no.1
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.71-78
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• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2361-2376
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• 2023

The electrocardiogram (ECG) signal is commonly used to screen and diagnose cardiovascular diseases. In recent years, deep neural networks have been regarded as an effective way for automatic ECG disease diagnosis. The convolutional neural network is widely used for ECG signal extraction because it can obtain different levels of information. However, most previous studies adopt single scale convolution filters to extract ECG signal features, ignoring the complementarity between ECG signal features of different scales. In the paper, we propose a dual-scale network with convolution filters of different sizes for 12-lead ECG classification. Our model can extract and fuse ECG signal features of different scales. In addition, different spatial and time periods of the feature map obtained from the 12-lead ECG may have different contributions to ECG classification. Therefore, we add a spatial-temporal attention to each scale sub-network to emphasize the representative local spatial and temporal features. Our approach is evaluated on PTB-XL dataset and achieves 0.9307, 0.8152, and 89.11 on macro-averaged ROC-AUC score, a maximum F1 score, and mean accuracy, respectively. The experiment results have proven that our approach outperforms the baselines.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.441-453
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• 2024

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.290-297
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• 2024

We investigate the efficacy of ensemble learning methods, specifically the soft voting technique, for enhancing heart disease prediction accuracy. Our study uniquely combines Logistic Regression, SVM with RBF Kernel, and Random Forest models in a soft voting ensemble to improve predictive performance. We demonstrate that this approach outperforms individual models in diagnosing heart disease. Our research contributes to the field by applying a well-curated dataset with normalization and optimization techniques, conducting a comprehensive comparative analysis of different machine learning models, and showcasing the superior performance of the soft voting ensemble in medical diagnosis. This multifaceted approach allows us to provide a thorough evaluation of the soft voting ensemble's effectiveness in the context of heart disease prediction. We evaluate our models based on accuracy, precision, recall, F1 score, and Area Under the ROC Curve (AUC). Our results indicate that the soft voting ensemble technique achieves higher accuracy and robustness in heart disease prediction compared to individual classifiers. This study advances the application of machine learning in medical diagnostics, offering a novel approach to improve heart disease prediction. Our findings have significant implications for early detection and management of heart disease, potentially contributing to better patient outcomes and more efficient healthcare resource allocation.

• Journal of Gastric Cancer
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• v.9 no.4
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• pp.177-185
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• 2009

Purpose: Preoperative clinical staging of gastric cancer is very important for determining the treatment plans and predicting the prognosis. The previous reports regarding the accuracy of computed tomography or endoscopic ultrasound for the preoperative staging of gastric cancer have shown various outcomes. We analyzed the diagnostic performance of CT and EUS, which are important staging tools for the staging of TN gastric cancer. Materials and Methods: We retrospectively analyzed 1,174 patients who underwent gastrectomy for gastric cancer at Seoul National University Bundang Hostpital from May, 2003 to December, 2007. We derived the Kappa value to examine the agreement of the preoperative staging obtained from CT and EUS with the pathological staging. Results: The mean age of the 1,174 patients was $59.31{\pm}11.98$ years. Six hundred thirty seven patients had early gastric cancer and 536 had advanced gastric cancer. The diagnostic performance between CT and EUS for the T staging showed no significant difference between CT and EUS for the kappa values. The kappa values showed moderate agreement at 0.4039 (P=0.021) and 0.4201 (P=0.026), respectively. This suggests that there is no difference between the two examinations for the overall T staging. Analysis of the discrimination of mucosal and submucosal lesions with EUS showed an accuracy of 58.92% and a Kappa value of 0.206 (P<0.001), suggesting fair agreement and a lower diagnostic performance than expected. To differentiate lesions with stages higher than or equal to T2 or T3 from the lesion with stages lower than T2 or T3, respectively, adoption of the higher stage from the CT staging or the EUS staging showed a larger AUC of 0.84 than that from either stage alone. The CT-derived node stage had the higher diagnostic performance (68.55%) than that of the EUS-derived node stage (60.82%) for the node staging. Conclusion: The CT-derived stage and EUS-derived stage showed comparable results for determining the T stage of gastric cancer. Yet the higher stage of the two stages from CT and EUS most accurately discriminated between those lesions with stages higher than T2 and those lesions with stages lower than T2.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.3
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• pp.1281-1287
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• 2015

Background: Early detection of hepatocellular carcinoma using serological markers with better sensitivity and specificity than alpha fetoprotein (AFP) is needed. Aims: The aim of this study was to evaluate the diagnostic value of serum sICAM-1, ${\beta}$-catenin, IL-8, proteasome and sTNFR-II in early detection of HCC. Materials and Methods: Serum levels of IL-8, sICAM-1, sTNFR-II, proteasome and ${\beta}$-catenin were measured by ELISA assay in 479 serum samples from 192 patients with HCC, 96 patients with liver cirrhosis (LC), 96 patients with chronic hepatitis C (CHC) and 95 healthy controls. Results: Serum levels of proteasome, sICAM-1, ${\beta}$-catenin and ${\alpha}FP$ were significantly elevated in HCC group compared to other groups (P-value<0.001), where serum level of IL-8 was significantly elevated in the LC and HCC groups compared to CHC and control groups (P-value <0.001), while no significant difference was noticed in patients with HCC and LC (P-value=0.09). Serum level of sTNFR-II was significantly elevated in patients with LC compared to HCC, CHC and control groups (P-value <0.001); also it was significantly higher in HCC compared to CHC and control groups (P-value <0.001). ROC curve analysis of the studied markers between HCC and other groups revealed that the serum level of proteasome had sensitivity of 75.9% and specificity of 73.4% at a cut-off value of $0.32{\mu}g/ml$ with AUC 0.803 sICAM-1 at cut off value of 778ng/ml, the sensitivity was 75.8% and the specificity was 71.8% with AUC 0.776. ${\beta}$-catenin had sensitivity and specificity of 70% and 68.6% respectively at a cut off value of 8.75ng/ml with an AUC of 0.729. sTNFR-II showed sensitivity of 86.3% and specificity of 51.8% at a cut off value of 6239.5pg/ml with an AUC of 0.722. IL-8 had sensitivity of 70.4% and specificity of 52.3% at a cut off value of 51.5pg/ml with AUC 0.631. Conclusions: Our data supported the role of proteasome, sICAM-1, sTNFR-II and ${\beta}$-catenin in early detection of HCC. Also, using this panel of serological markers in combination with ${\alpha}FP$ may offer improved diagnostic performance over ${\alpha}FP$ alone in the early detection of HCC.

• Journal of the korean academy of Pediatric Dentistry
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• v.42 no.1
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• pp.30-37
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• 2015

A new colorimetric test ($Cariview^{(R)}$) using a new type of pH indicator can reflect the acidogenic potential of plaque bacteria. The objective of this study was to evaluate the correlation between $Cariview^{(R)}$ and the caries experience (measured through the dmft index) of children, and to compare $Cariview^{(R)}$ with Dentocult $SM^{(R)}$. Having obtained informed consent, 135 children less than 6 years old participated in the study. We examined their dmft index, and performed two caries activity tests ($Cariview^{(R)}$ and Dentocult $SM^{(R)}$) according to the manufacturers' instructions. In the results, $Cariview^{(R)}$ showed a moderate correlation with the dmft index (r = 0.43, p < 0.01). $Cariview^{(R)}$ showed a sensitivity of 68.8%, a specificity of 69.2%, and an area under curve of 0.686 in the ROC curve analysis. $Cariview^{(R)}$ had a significant correlation with the children's caries experience and had a slightly better explanatory ability than Dentocult $SM^{(R)}$. Furthermore, $Cariview^{(R)}$ was convenient and easy to use on uncooperative children, and also had an educational effect with its visual colors. It is suggested that $Cariview^{(R)}$ could be used clinically to identify the children susceptible to develop caries and to establish a preventive strategy.