Genetic testing and personalized ovarian cancer screening: a survey of public attitudes

Background Advances in genetic technologies are expected to make population-wide genetic testing feasible. This could provide a basis for risk stratified cancer screening; but acceptability in the target populations has not been explored. Methods We assessed attitudes to risk-stratified ovarian cancer (OC) screening based on prior genetic risk assessment using a survey design. Home-based interviews were carried out by the UK Office of National Statistics in a population-based sample of 1095 women aged 18–74. Demographic and personal correlates of attitudes to risk-stratified OC screening based on prior genetic risk assessment were determined using univariate analyses and adjusted logistic regression models. Results Full data on the key analytic questions were available for 829 respondents (mean age 46 years; 27 % ‘university educated’; 93 % ‘White’). Relatively few respondents felt they were at ‘higher’ or ‘much higher’ risk of OC than other women of their age group (7.4 %, n = 61). Most women (85 %) said they would ‘probably’ or ‘definitely’ take up OC genetic testing; which increased to 88 % if the test also informed about breast cancer risk. Almost all women (92 %) thought they would ‘probably’ or ‘definitely’ participate in risk-stratified OC screening. In multivariate logistic regression models, university level education was associated with lower anticipated uptake of genetic testing (p = 0.009), but with more positive attitudes toward risk-stratified screening (p <0.001). Perceived risk was not significantly associated with any of the outcome variables. Conclusions These findings give confidence in taking forward research on integration of novel genomic technologies into mainstream healthcare

Prediction schizophrenia using random forest

Schizophrenia is a mental illness with a very bad impact on sufferers, attacking the part of human brain that disables the ability to think clearly. In 2018, Rustam and Rampisela classified Schizophrenia by using Northwestern University Schizophrenia Data, based on 66 variables consisting of group, demographic, and questionnaires statistics, based on the scale for the assessment of negative symptoms (SANS), and scale for the assessment of positive symptoms (SAS), and then classifiers that used are SVM with Gaussian kernel and Twin SVM with linear and Gaussian kernel. Furthermore, this research is novel based on the use of random forest as a classifier, in order to predict Schizophrenia. The result obtained is reported in percentage of accuracy, both in training and testing of random forest, which was 100%. This classification, therefore, shows the best value in contrast with prior methods, even though only 40% of training data set was used. This is very important, especially in the cases of rare disease, including schizophrenia

Why do we need to employ Bayesian statistics and how can we employ it in studies of moral education?: With practical guidelines to use JASP for educators and researchers

ABSTRACTIn this article, we discuss the benefits of Bayesian statistics and how to utilize them in studies of moral education. To demonstrate concrete examples of the applications of Bayesian statistics to studies of moral education, we reanalyzed two data sets previously collected: one small data set collected from a moral educational intervention experiment, and one big data set from a large-scale Defining Issues Test-2 survey. The results suggest that Bayesian analysis of data sets collected from moral educational studies can provide additional useful statistical information, particularly that associated with the strength of evidence supporting alternative hypotheses, which has not been provided by the classical frequentist approach focusing on P-values. Finally, we introduce several practical guidelines pertaining to how to utilize Bayesian statistics, including the utilization of newly developed free statistical software, Jeffrey’s Amazing Statistics Program, and thresholding based on Bayes Factors, to scholars in the field of moral education