Medical diagnoses have important implications for improving patient care, research, and policy. For a medical diagnosis, health professionals use different kinds of pathological methods to make decisions on medical reports in terms of the patients’ medical conditions. Recently, clinicians have been actively engaged in improving medical diagnoses. The use of artificial intelligence and machine learning in combination with clinical findings has further improved disease detection. In the modern era, with the advantage of computers and technologies, one can collect data and visualize many hidden outcomes such as dealing with missing data in medical research. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning (ML), data-driven algorithms can be utilized to validate existing methods and help researchers to make potential new decisions. The purpose of this study was to extract significant predictors for liver disease from the medical analysis of 615 humans using ML algorithms. Data visualizations were implemented to reveal significant findings such as missing values. Multiple imputations by chained equations (MICEs) were applied to generate missing data points, and principal component analysis (PCA) was used to reduce the dimensionality. Variable importance ranking using the Gini index was implemented to verify significant predictors obtained from the PCA. Training data (ntrain=399) for learning and testing data (ntest=216) in the ML methods were used for predicting classifications. The study compared binary classifier machine learning algorithms (i.e., artificial neural network, random forest (RF), and support vector machine), which were utilized on a published liver disease data set to classify individuals with liver diseases, which will allow health professionals to make a better diagnosis. The synthetic minority oversampling technique was applied to oversample the minority class to regulate overfitting problems. The RF significantly contributed (p<0.001) to a higher accuracy score of 98.14% compared to the other methods. Thus, this suggests that ML methods predict liver disease by incorporating the risk factors, which may improve the inference-based diagnosis of patients.
Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (ANN) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (MSEs) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.
Background: To investigate the association of optic neuritis (ON) after the COVID-19 vaccines. Methods: Cases of ON from Vaccine Adverse Event Reporting System (VAERS) were collected and divided into the prepandemic, COVID-19 pandemic, and COVID-19 vaccine periods. Reporting rates were calculated based on estimates of vaccines administered. Proportion tests and Pearson χ2 test were used to determine significant differences in reporting rates of ON after vaccines within the 3 periods. Kruskal–Wallis testing with Bonferroni-corrected post hoc analysis and multivariable binary logistic regression was used to determine significant case factors such as age, sex, concurrent multiple sclerosis (MS) and vaccine manufacturer in predicting a worse outcome defined as permanent disability, emergency room (ER) or doctor visits, and hospitalizations. Results: A significant increase in the reporting rate of ON after COVID-19 vaccination compared with influenza vaccination and all other vaccinations (18.6 vs 0.2 vs 0.4 per 10 million, P < 0.0001) was observed. However, the reporting rate was within the incidence range of ON in the general population. Using self-controlled and case-centered analyses, there was a significant difference in the reporting rate of ON after COVID-19 vaccination between the risk period and control period (P < 0.0001). Multivariable binary regression with adjustment for confounding variables demonstrated that only male sex was significantly associated with permanent disability. Conclusions: Some cases of ON may be temporally associated with the COVID-19 vaccines; however, there is no significant increase in the reporting rate compared with the incidence. Limitations of this study include those inherent to any passive surveillance system. Controlled studies are needed to establish a clear causal relationship.
In this project, we use a statistical multiple regression to study the impact of eight various predictors (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) to estimate the cooling load energy efficiency of residential buildings. We try to analyze and visualize the effect of each predictor with each of the response variable using different classical statistical analysis tools used in describing linear models, in such a way so that we can find out the most strongly related predictor variables. Before starting all of this, we use the idea of model selection by stepwise regression technique and compare the AIC of these models and identified a better model between all of them. Then, we compare a classical linear regression approach by simulations on 768 diverse residential buildings show that we can predict CL with low mean absolute error. By using ANOVA we determine variation in the different residuals. Also, we use non constant variance test to verify it. Furthermore, we check leverage and influence points as well as outliers as well as determined cook distance for influential points. By taking box cox transformation and weights, we also introduce WLS technique to fit the model for better results and did all type of important analysis to understand the energy efficiency. Finally, we show 5-fold cross validation to verify our model.
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