• Asian Pacific Journal of Cancer Prevention
• /
• 제14권11호
• /
• pp.6673-6680
• /
• 2013

Objective: Insulin resistance (IR) is an established risk factor for colorectal cancer (CRC). Given that CRC and IR physiologically overlap and the calpain-10 gene (CAPN10) is a candidate for IR, we explored the association between CAPN10 and CRC risk. Methods: Blood samples of 400 case-control pairs were genotyped, and the lifestyle and dietary habits of these pairs were recorded and collected. Unconditional logistic regression (LR) was used to assess the effects of CAPN10 SNP43 and SNP19, and environmental factors. Both generalized multifactor dimensionality reduction (GMDR) and the classification and regression tree (CART) were used to test gene-environment interactions for CRC risk. Results: The GA+AA genotype of SNP43 and the Del/Ins+Ins/Ins genotype of SNP19 were marginally related to CRC risk (GA+AA: OR = 1.35, 95% CI = 0.92-1.99; Del/Ins+Ins/Ins: OR = 1.31, 95% CI = 0.84-2.04). Notably, a high-order interaction was consistently identified by GMDR and CART analyses. In GMDR, the four-factor interaction model of SNP43, SNP19, red meat consumption, and smoked meat consumption was the best model, with a maximum cross-validation consistency of 10/10 and testing balance accuracy of 0.61 (P < 0.01). In LR, subjects with high red and smoked meat consumption and two risk genotypes had a 6.17-fold CRC risk (95% CI = 2.44-15.6) relative to that of subjects with low red and smoked meat consumption and null risk genotypes. In CART, individuals with high smoked and red meat consumption, SNP19 Del/Ins+Ins/Ins, and SNP43 GA+AA had higher CRC risk (OR = 4.56, 95%CI = 1.94-10.75) than those with low smoked and red meat consumption. Conclusions: Though the single loci of CAPN10 SNP43 and SNP19 are not enough to significantly increase the CRC susceptibility, the combination of SNP43, SNP19, red meat consumption, and smoked meat consumption is associated with elevated risk.

• Asian Pacific Journal of Cancer Prevention
• /
• 제16권12호
• /
• pp.5095-5099
• /
• 2015

Background: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. Materials and Methods: A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. Results: The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05%(200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (${\geq}65$ years), use of antibiotics, low serum albumin concentrations (${\leq}37.18g/L$), radiotherapy, surgery, low hemoglobin hyperlipidemia (${\leq}93.67g/L$), long time of hospitalization (${\geq}14$days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model($0.829{\pm}0.019$)was higher than that of LR model ($0.756{\pm}0.021$). Conclusions: The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

• 보건행정학회지
• /
• 제5권2호
• /
• pp.155-172
• /
• 1995

This study is an empirical analysis on the equity in the delivery of heatlh care under the Korean Medical Insurance Corporation System. The purposes of this study are to find out effects of income on the health care utiliztion and measure the income-related inequity in the distribution of health care. This study was carried out based on the fact that the health insurance program has been organized to achieve the equity objective, "equal treatment for equal needs". Of 41, 828 insured persons who had been diagnosed in the 1993 Health Screening Test and utilifzation data from 1, January 1993 through 31, December 1993 were derived from the Benefit Managment File. Inequity was measured by means of I) share approach, ii) standardization concentration curve approach, iii) inequity index, iv) test for inequity. The major findings were as follows : 1. The expenditure shares of the top two quintile groups exceeded their morbidity shares, whereas the opposite was true of the bottom three quintile groups, Which showed a positive HI$_{LG}$ inequity index, suggesting the presence of some inequity favoring the rich group. 2. Compared with other residential areas, the rural area showed the highest positive HI$_{LG}$ irrespective of need indicatior applied. 3. Standardized expenditure concentration indices adjusted by age, gender and need structure were also found to be positive, and therefore still indicated that there has been inequity favoring the rich after the standardization. 4. The Loglikelihood Ratio (LR) test for the statistical significance of income-related inequity of medical care utilization was carried out using the logistic regression model. The resulting loglikelihood ratio test statistic value was 176, which did exceed the 0.5 percent critical value of the chi-square distribution with 28 degrees of freedom, which is 50.993. Therefore, the null hypothesis of no income-related inequity of medical care utilization was rejected at the 99.5 percent confidence level. 5. The Regression based F-test has been carried out for analyzing the income-related inequity of medical expenditure in terms of age, gender, morbidity indicators as explanary variables. The hypothesis of the absence of income-relate inequity was rejected for all need indicators at the 95% confidence level.nce level.

• Geomechanics and Engineering
• /
• 제28권6호
• /
• pp.599-611
• /
• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• 대한공간정보학회지
• /
• 제21권2호
• /
• pp.107-116
• /
• 2013

본 연구는 새로운 기후변화 시나리오인 RCP 시나리오의 스토리라인을 기반으로 미래 토지피복변화를 예측하고, RCP 시나리오하의 미래 기후 및 토지피복 변화가 유역 내 유출량에 미치는 영향을 분석하는데 그 목적을 둔다. RCP 4.5 및 8.5하의 기후 자료가 기후변화 시나리오로 사용되었고, 토지피복변화 시나리오는 RCP 4.5 및 8.5 시나리오의 스토리라인과 로지스틱 회귀모형(LR)을 이용하여 개발된 모델에 의해 생성되었다. 기후변화만 고려한 경우, 토지피복변화만 고려한 경우로 두 가지 시나리오를 설정하고, 각각의 시나리오에 따른 대상 유역 내 유출량을 모의한 결과는 유출량의 계절적 변화를 뚜렷이 나타내었다. 기후변화는 봄과 겨울에 유출량을 증가, 여름과 가을에 유출량을 감소시키는 것으로 예측되었다. 반면 토지피복변화는 기후변화에 비해 상대적으로 유역 내 유출량 변화에 미소한 영향을 주지만, 강수 유무에 따라 유출량의 증가 및 감소 패턴이 뚜렷이 나타났다. 따라서 수자원 정책결정에 있어서 미래 토지피복변화에 따른 홍수 및 가뭄의 패턴에 적합한 수자원 정책이 필요할 것으로 판단된다.

• 디지털융복합연구
• /
• 제17권3호
• /
• pp.107-117
• /
• 2019

블로그, 소셜 미디어 등의 발달로 인해 점점 더 많은 사람들이 본인의 의견이나 감정을 표현하기 위해 온라인상에서 텍스트 문장을 작성한다. 그리고 이같은 온라인 텍스트 문장속에 숨겨져 있는 긍정 또는 부정등의 감성을 찾아내는 연구분야를 감성분석 이라고 한다. 그중에서도 이모션 마이닝은 사람들의 구체적인 이모션을 찾아내는데 초점을 맞춘 연구분야이다. 본 연구에서는 속성선택 방법과 단일 및 앙상블 분류기를 조합하여 효과적인 이모션 마이닝 예측모델을 제시하고자 한다. 이를 위해 두가지 대표적인 오픈 데이터인 Tweet와 SemEval2007 데이터를 이용하여 TF-IDF를 계산하고 백 오브 워즈(BOW: bag-of-words) 형태로 속성 셋을 구성하였다. 그리고 효과적인 이모션 마이닝이 될 수 있는 최적의 속성을 선택하기 위하여 상관관계 기반 속성선택(CFS), 정보획득 속성선택 (IG), 그리고 ReliefF 등 세가지 속성선택 방법을 적용하였다. 선택된 속성을 이용하여 아홉가지 분류기 모델로 이모션 마이닝의 정확도를 비교하였다. 실험 결과, Tweet 데이터는 의사결정나무(DT)가 CFS, IG, ReliefF에 의한 속성을 이용할 경우 정확도가 상승했고, 랜덤서브스페이스(RS)는 CFS, IG에 선택된 속성을 사용할 경우 정확도가 상승했다. SemEval2007 데이터는 ReliefF에 의해 선택된 속성으로 로지스틱 회귀분석(LR)을 적용하였을 때 정확도가 상승했고, 나이브 베이지안 네트워크(NBN)은 CFS, IG에 의한 속성을 사용할 경우 정확도가 상승하였다.

• Asian Pacific Journal of Cancer Prevention
• /
• 제15권13호
• /
• pp.5349-5353
• /
• 2014

Statistical methods to analyze and predict the related risk factors of nosocomial infection in lung cancer patients are various, but the results are inconsistent. A total of 609 patients with lung cancer were enrolled to allow factor comparison using Student's t-test or the Mann-Whitney test or the Chi-square test. Variables that were significantly related to the presence of nosocomial infection were selected as candidates for input into the final ANN model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of nosocomial infection from lung cancer in this entire study population was 20.1% (165/609), nosocomial infections occurring in sputum specimens (85.5%), followed by blood (6.73%), urine (6.0%) and pleural effusions (1.82%). It was shown that long term hospitalization (${\geq}22days$, P= 0.000), poor clinical stage (IIIb and IV stage, P=0.002), older age (${\geq}61days$ old, P=0.023), and use the hormones were linked to nosocomial infection and the ANN model consisted of these four factors. The artificial neural network model with variables consisting of age, clinical stage, time of hospitalization, and use of hormones should be useful for predicting nosocomial infection in lung cancer cases.

• Asian Pacific Journal of Cancer Prevention
• /
• 제17권6호
• /
• pp.2811-2819
• /
• 2016

Multiple genetic and environmental factors have been reported to play key role in the development of nasopharyngeal carcinoma (NPC). Here, we investigated interactions of XRCC1 Arg399Gln and XRCC2 Arg188His polymorphisms and environmental factors in modulating susceptibility to NPC in Northeast India. One-hundred NPC patients, 90 first-degree relatives of patients and 120 controls were enrolled in the study. XRCC1 Arg399Gln and XRCC2 Arg188His polymorphisms were determined using PCR-RFLP, and the results were confirmed by DNA sequencing. Logistic regression (LR) and multifactor dimensionality reduction (MDR) approaches were applied for statistical analysis. The XRCC1 Gln/Gln genotype showed increased risk (OR=2.76; P<0.024) of NPC. However, individuals with both XRCC1 and XRCC2 polymorphic variants had 3.2 fold elevated risk (P<0.041). An enhanced risk of NPC was also observed in smoked meat (OR=4.07; P=0.004) and fermented fish consumers (OR=4.34, P=0.001), and tobacco-betel quid chewers (OR=7.00; P=0.0001) carrying XRCC1 polymorphic variants. However, smokers carrying defective XRCC1 gene showed the highest risk (OR = 7.47; P<0.0001). On MDR analysis, the best model for NPC risk was the five-factor model combination of XRCC1 variant genotype, fermented fish, smoked meat, smoking and chewing (CVC=10/10; TBA=0.636; P<0.0001); whereas in interaction entropy graphs, smoked meat and tobacco chewing showed synergistic interactions with XRCC1. These findings suggest that interaction of genetic and environmental factors might increase susceptibility to NPC in Northeast Indian populations.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권2호
• /
• pp.370-396
• /
• 2024

COVID-19 was declared a pandemic by the World Health Organization (WHO) on 30 January 2020. The lifestyle of people all over the world has changed since. In most cases, the pandemic has appeared to create severe mental disorders, anxieties, and depression among people. Mostly, the researchers have been conducting surveys to identify the impacts of the pandemic on the mental health of people. Despite the better quality, tailored, and more specific data that can be generated by surveys,social media offers great insights into revealing the impact of the pandemic on mental health. Since people feel connected on social media, thus, this study aims to get the people's sentiments about the pandemic related to mental issues. Word Cloud was used to visualize and identify the most frequent keywords related to COVID-19 and mental health disorders. This study employs Majority Voting Ensemble (MVE) classification and individual classifiers such as Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) to classify the sentiment through tweets. The tweets were classified into either positive, neutral, or negative using the Valence Aware Dictionary or sEntiment Reasoner (VADER). Confusion matrix and classification reports bestow the precision, recall, and F1-score in identifying the best algorithm for classifying the sentiments.

• International Journal of Computer Science & Network Security
• /
• 제24권7호
• /
• pp.108-117
• /
• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.