• Journal of KIISE:Computer Systems and Theory
• /
• v.34 no.7
• /
• pp.276-281
• /
• 2007

In this paper, we use Support Vector Machine to predict the susceptibility of chronic hepatitis from single nucleotide polymorphism data. Our data set consists of SNP data for 328 patients based on 28 SNPs and patients classes(chronic hepatitis, healthy). We use leave-one-out cross validation method for estimation of the accuracy. The experimental results show that SVM with SNP is capable of classifying the SNP data successfully for chronic hepatitis susceptibility with accuracy value of 67.1%. The accuracy of all SNPs with health related feature(sex, age) is improved more than 7%(accuracy 74.9%). This result shows that the accuracy of predicting susceptibility can be improved with health related features. With more SNPs and other health related features, SVM prediction of SNP data is a potential tool for chronic hepatitis susceptibility.

• International Journal of Computer Science & Network Security
• /
• v.22 no.1
• /
• pp.167-174
• /
• 2022

In metaheuristic algorithms such as Genetic Algorithm (GA), initial population has a significant impact as it affects the time such algorithm takes to obtain an optimal solution to the given problem. In addition, it may influence the quality of the solution obtained. In the machine learning field, feature selection is an important process to attaining a good performance model; Genetic algorithm has been utilized for this purpose by scientists. However, the characteristics of Genetic algorithm, namely random initial population generation from a vector of feature elements, may influence solution and execution time. In this paper, the use of a statistical algorithm has been introduced (Chi2) for feature relevant checks where p-values of conditional independence were considered. Features with low p-values were discarded and subject relevant subset of features to Genetic Algorithm. This is to gain a level of certainty of the fitness of features randomly selected. An ensembled-based learning model for Hepatitis has been developed for Hepatitis C stage classification. 1385 samples were used using Egyptian-dataset obtained from UCI repository. The comparative evaluation confirms decreased in execution time and an increase in model performance accuracy from 56% to 63%.

• Molecules and Cells
• /
• v.40 no.6
• /
• pp.418-425
• /
• 2017

Interferon-${\gamma}$-inducible protein 10 (IP-10), also known as chemokine C-X-C motif ligand (CXCL) 10, is closely associated with antiviral immunity and the progression of chronic hepatitis B (CHB). However, the value of baseline serological and histological IP-10 expression levels in predicting the efficacy of the antiviral response to nucleoside/nucleotide analogues (NAs) is still unknown. In our research, intrahepatic and peripheral IP-10 expression levels were systemically examined before and after treatment with entecavir (ETV). Baseline serological and histological IP-10 expression levels were significantly increased in patients with CHB, particularly in patients with higher degrees of liver inflammation and liver fibrosis. Moreover, higher baseline intrahepatic IP-10 levels indicated better prognoses in patients with CHB after entecavir therapy. The baseline IP-10 level was also positively associated with several clinical parameters, including baseline levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), hepatitis B virus (HBV) DNA, and hepatitis B surface antigen (HBsAg), and with the decrease in HBsAg levels after treatment. In addition, monocyte-derived IP-10 was expressed at higher levels in patients with CHB than in patients with liver cirrhosis (LC) and healthy controls (HC). According to the results of our in vitro experiments, IP-10 directly promoted hepatocyte apoptosis. Based on these findings, baseline serological and histological IP-10 levels might predict CHB severity and the decrease in HBsAg levels after entecavir therapy.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.1
• /
• pp.381-385
• /
• 2015

Background:Hepatocellular carcinoma (HCC) is the second most common malignancy in Egypt. Data mining is a method of predictive analysis which can explore tremendous volumes of information to discover hidden patterns and relationships. Our aim here was to develop a non-invasive algorithm for prediction of HCC. Such an algorithm should be economical, reliable, easy to apply and acceptable by domain experts. Methods: This cross-sectional study enrolled 315 patients with hepatitis C virus (HCV) related chronic liver disease (CLD); 135 HCC, 116 cirrhotic patients without HCC and 64 patients with chronic hepatitis C. Using data mining analysis, we constructed a decision tree learning algorithm to predict HCC. Results: The decision tree algorithm was able to predict HCC with recall (sensitivity) of 83.5% and precession (specificity) of 83.3% using only routine data. The correctly classified instances were 259 (82.2%), and the incorrectly classified instances were 56 (17.8%). Out of 29 attributes, serum alpha fetoprotein (AFP), with an optimal cutoff value of ${\geq}50.3ng/ml$ was selected as the best predictor of HCC. To a lesser extent, male sex, presence of cirrhosis, AST>64U/L, and ascites were variables associated with HCC. Conclusion: Data mining analysis allows discovery of hidden patterns and enables the development of models to predict HCC, utilizing routine data as an alternative to CT and liver biopsy. This study has highlighted a new cutoff for AFP (${\geq}50.3ng/ml$). Presence of a score of >2 risk variables (out of 5) can successfully predict HCC with a sensitivity of 96% and specificity of 82%.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.10
• /
• pp.5891-5893
• /
• 2013

Incidence/mortality of liver cancer follow logistic curves because there is a limit reflecting the prevalence of hepatitis virus carriers in the cohort. The author fitted logistic curves to incidence/mortality data covering the nine five-year cohorts born in 1911-1955 of both sexes. Goodness-of-fit of logistic curves was sufficiently precise to be used for future predictions. Younger cohorts born in 1936 or later were predicted to show constant decline in incidence/mortality in the future. The male cohort born in 1931-35 showed an elevated incidence/mortality of liver cancer early in their lives supporting the previous claim that this particular cohort had suffered massive HCV infection due to nation-wide drug abuse in the 1950s. Declining case-fatality observed in younger cohorts suggested improved treatment of liver cancer. This study demonstrated that incidence/mortality of liver cancer follow logistic curves and fitted logistic formulae can be used for future prediction. Given the predicted decline of incidence/mortality in younger cohorts, liver cancer is likely to be lost to history in the not-so-distant future.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.11
• /
• pp.6721-6726
• /
• 2013

Background: Changes in DNA methylation patterns are believed to be early events in hepatocarcinogenesis. A better understanding of methylation states and how they correlate with disease progression will aid in finding potential strategies for early detection of HCC. The aim of our study was to analyze the methylation frequency of tumor suppressor genes, P14, P15, and P73, and a mismatch repair gene (O6MGMT) in HCV related chronic liver disease and HCC to identify candidate epigenetic biomarkers for HCC prediction. Materials and Methods: 516 Egyptian patients with HCV-related liver disease were recruited from Kasr Alaini multidisciplinary HCC clinic from April 2010 to January 2012. Subjects were divided into 4 different clinically defined groups - HCC group (n=208), liver cirrhosis group (n=108), chronic hepatitis C group (n=100), and control group (n=100) - to analyze the methylation status of the target genes in patient plasma using EpiTect Methyl qPCR Array technology. Methylation was considered to be hypermethylated if >10% and/or intermediately methylated if >60%. Results: In our series, a significant difference in the hypermethylation status of all studied genes was noted within the different stages of chronic liver disease and ultimately HCC. Hypermethylation of the P14 gene was detected in 100/208 (48.1%), 52/108 (48.1%), 16/100 (16%) and 8/100 (8%) among HCC, liver cirrhosis, chronic hepatitis and control groups, respectively, with a statistically significant difference between the studied groups (p-value 0.008). We also detected P15 hypermethylation in 92/208 (44.2%), 36/108 (33.3%), 20/100 (20%) and 4/100 (4%), respectively (p-value 0.006). In addition, hypermethylation of P73 was detected in 136/208 (65.4%), 72/108 (66.7%), 32/100 (32%) and 4/100 (4%) (p-value <0.001). Also, we detected O6MGMT hypermethylation in 84/208 (40.4%), 60/108 (55.3%), 20/100 (20%) and 4/100 (4%), respectively (p value <0.001. Conclusions: The epigenetic changes observed in this study indicate that HCC tumors exhibit specific DNA methylation signatures with potential clinical applications in diagnosis and prognosis. In addition, methylation frequency could be used to monitor whether a patient with chronic hepatitis C is likely to progress to liver cirrhosis or even HCC. We can conclude that methylation processes are not just early events in hepatocarcinogenesis but accumulate with progression to cancer.

• Journal of the Korean Society of Radiology
• /
• v.17 no.7
• /
• pp.1057-1065
• /
• 2023

Non-invasive liver fibrosis diagnosis is crucial for patients with chronic liver diseases. Many patients cannot undergo liver tissue biopsy, so predicting the degree of liver fibrosis early through meaningful methods can reduce complications related to chronic liver diseases, such as liver cell carcinoma and cirrhosis. This study compared and analyzed the quantitative measurement of liver fibrosis using shear wave elastography in conjunction with liver ultrasound findings and their associations with serum biomarkers (p<0.05). The results showed that the shear wave elastography measurement in the normal group was 4.55 ± 0.69 kPa, while the abnormal contrast group with echogenic patterns had a measurement of 8.27 ± 1.83 kPa. The hepatitis B carrier group exhibited higher shear wave elastography measurements, and among serum biomarkers, AST, ALT, GGT, and PT showed statistically significant positive correlations with fibrosis severity according to SWE categories (p<0.05), while ALP and TB did not demonstrate statistically significant differences (p=0.163, p=0.567). Conversely, Albumin and PLT showed significant negative correlations (p<0.05). Clinically, utilizing shear wave elastography measurements through liver ultrasound in the tracking and repeat testing of liver fibrosis in chronic hepatitis B patients without cirrhosis can assist in achieving more objective diagnoses among healthcare providers.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.21 no.4
• /
• pp.289-296
• /
• 2018

Purpose: We aimed to study the pattern of liver-injury in children with dengue fever (DF) and validate serum aminotransferase ${\geq}1,000IU/L$ as a marker of severe DF. Methods: Children admitted with DF were included. DF was defined by presence of clinical criteria and positive serological or antigen tests in absence of other etiology. DF severity was graded as dengue without or with warning signs and severe dengue. Liver-injury was defined as alanine aminotransferase (ALT) more than twice the upper limit of normal (boys, 30 IU/L; girls, 21 IU/L). Results: Of 372 children with DF, 144 (38.7%) had liver-injury. Risk of liver-injury and aminotransferase levels increased with DF severity (p<0.001). Recommended ALT and aspartate aminotransferase (AST) cut-off at ${\geq}1,000IU/L$ had sensitivity 4.8% (5/105), specificity 99.3% (265/267) for detection of severe DF. In children with ALT and AST <1,000 IU/L (n=365), the area under receiver operating curves for prediction for severe DF, were 0.651 (95% confidence interval [CI], 0.588-0.714; p<0.001) for ALT and 0.647 (95% CI, 0.582-0.712; p<0.001) for AST. Serum ALT at 376 IU/L and AST at 635 IU/L had sensitivity and specificity comparable to ${\geq}1,000IU/L$ for defining severe DF. Conclusion: Liver-injury is common in DF. The ALT and AST levels increase with DF severity. ALT and AST levels of ${\geq}1,000IU/L$ could be lowered to 376 IU/L and 635 IU/L respectively for defining severe DF.

• YAKHAK HOEJI
• /
• v.55 no.4
• /
• pp.352-360
• /
• 2011

Standard combination chemotherapy including isoniazid, rifampin, pyrazinamide, and ethambutol is very effective against tuberculosis. But, these medicines can cause hepatotoxicity which is the main reason for treatment interruption or change in drug regimen. In order to identify risk factors associated with hepatotoxcity in Koreans and assess elevated baseline LFTs' contributions to hepatotoxicity, a retrospective case control study was performed. The medical records of 277 patients who diagnosed with tuberculosis at a community hospital from January 1st, 2007 to June 30th, 2010 were reviewed. Patients were categorized into 3 groups (non toxic group, patients without increase in LFT levels; mild to moderate hepatotoxic group and severe hepatotoxic group). And the correlation between risk factors and hepatotoxicity was analyzed by using SPSS program. The overall incidence of hepatotoxicity was 18% and 8.7% of patients developed severe toxicity. Patients in the severe toxic group had the longest treatment period among the three groups. In 75% of severe toxic group, hepatotoxicity occurred within 18.3 days after starting medication. Hypoalbuminemia (serum albumin <3 g/dl) was a significant risk factor for development of severe toxicity. Elevated baseline transaminase (except ALT), total bilirubin, and preexisting hepatitis were also risk factors which were more than twice as likely to increase risk of severe hepatotoxicity (p>0.05). In conclusion, hypoalbuminemia (serum albumin level <3 g/dl) was a significant risk factor for anti-tuberculosis druginduced severe toxicity. Therefore, before starting antituberculosis chemotherapy, serum albumin level should be assessed at baseline. In high-risk patients (hypoalbuminemia, elevated LFTs) for hepatotoxicty, liver function should be closely monitored up to at least 21 days after taking medication.

• Sleep Medicine and Psychophysiology
• /
• v.24 no.1
• /
• pp.38-45
• /
• 2017

Objectives: Sleep disturbance is a very rapidly growing disease with aging. The purpose of this study was to investigate the prevalence of sleep disturbances and its predictive factors in a three-year cohort study of people aged 60 years and over in Korea. Methods: In 2012 and 2014, we obtained data from a survey of the Korean Social Life, Health, and Aging Project. We asked participants if they had been diagnosed with stroke, myocardial infarction, angina pectoris, arthritis, pulmonary tuberculosis, asthma, cataract, glaucoma, hepatitis B, urinary incontinence, prostate hypertrophy, cancer, osteoporosis, hypertension, diabetes, hyperlipidemia, or metabolic syndrome. Cognitive function was assessed using the Mini-Mental State Examination for dementia screening in 2012, and depression was assessed using the Center for Epidemiologic Studies Depression Scale in 2012 and 2014. In 2015, a structured clinical interview for Axis I psychiatric disorders was administered to 235 people, and sleep disturbance was assessed using the Pittsburgh Sleep Quality Index. The perceived stress scale and the State-trait Anger Expression Inventory were also administered. Logistic regression analysis was used to predict sleep disturbance by gender, age, education, depression score, number of coexisting diseases in 2012 and 2014, current anger score, and perceived stress score. Results: Twenty-seven percent of the participants had sleep disturbances. Logistic regression analysis showed that the number of medical diseases three years ago, the depression score one year ago, and the current perceived stress significantly predicted sleep disturbances. Conclusion: Comorbid medical disease three years previous and depressive symptoms evaluated one year previous were predictive of current sleep disturbances. Further studies are needed to determine whether treatment of medical disease and depressive symptoms can improve sleep disturbances.