Adverse Event Risk Assessment on Patients Receiving Combination Antiretroviral Therapy in South Africa

Purpose: To determine the risk factors for the development of serious adverse events (AEs) in black adult patients on combination antiretroviral therapy (cART). Methods: This prospective cohort study consisted of 368 adult black HIV positive patients receiving cART at the Grey's Hospital, KwaZulu-Natal, South Africa. Patients were intensively monitored for incidence of adverse events and the factors associated with their development, under the Antiretroviral Cohort Adverse Event Monitoring in KwaZulu-Natal (ACADEMIK). Multiple logistic regression models were used to identify the risk factors for AEs. Results: A total of 406 AEs were reported across the 13 patient hospital visits in the study. Peripheral neuropathy was the most prevalent adverse event (16%), followed by hypercholesterolaemia (14%), lipoatrophy/lipodystrophy (13%) and skin reaction (11%). Cluster differentiation (CD4) counts (p = 0.0280), age (p = 0.0227) and weight (p = 0.0017) were identified as the significant predictors for hypercholesterolaemia, while sex (p = 0.0309) was significant with respect to skin reaction. CD4 counts (p=0.0200) was also significant for lipoatrophy/lipodystrophy. Skin reaction (23%), diarrhea (18%), hypercholesterolaemia (15%), thrombocytopenia (15%) and peripheral neuropathy (13%) were the top five most incident AEs. Overall, about 46% of the regimens administered were tenofovir-based and 31% zidovudine-based. Conclusions: To enhance the prevention of hypercholesterolaemia, lipoatrophy/lipodystrophy and skin reaction among black adult HIV positive patients on cART, we recommend that CD4 counts and weight be closely monitored and documented during clinic visits

Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model

Abstract This study conducted a comprehensive analysis of multiple supervised machine learning models, regressors and classifiers, to accurately predict diamond prices. Diamond pricing is a complex task due to the non-linear relationships between key features such as carat, cut, clarity, table, and depth. The analysis aimed to develop an accurate predictive model by utilizing both regression and classification approaches. To preprocess the data, the study employed various techniques. The work addressed outliers, standardized the predictors, performed median imputation of missing values, and resolved multicollinearity issues. Equal-width binning on the cut variable was performed to handle class imbalance. Correlation-based feature selection was utilized to eliminate highly correlated variables, ensuring that only relevant features were included in the models. Outliers were handled using the inter-quartile range method, and numerical features were normalized through standardization. Missing values in numerical features were imputed using the median, preserving the integrity of the dataset. Among the models evaluated, the RF regressor exhibited exceptional performance. It achieved the lowest root mean squared error (RMSE) of 523.50, indicating superior accuracy compared to the other models. The RF regressor also obtained a high R-squared ( $$\text {R}^2$$ R 2 ) score of 0.985, suggesting it explained a significant portion of the variance in diamond prices. Furthermore, the area under the curve with RF classifier for the test set was 1.00 $$\, (100\%)$$ ( 100 % ) , indicating perfect classification performance. These results solidify the RF’s position as the best-performing model in terms of accuracy and predictive power, both in regression and classification. The MLP regressor showed promising results with an RMSE of 563.74 and an $$\text {R}^2$$ R 2 score of 0.980, demonstrating its ability to capture the complex relationships in the data. Although it achieved slightly higher errors than the RF regressor, further analysis is needed to determine its suitability and potential advantages compared to the RF regressor. The XGBoost Regressor achieved an RMSE of 612.88 and an $$\text {R}^2$$ R 2 score of 0.972, indicating its effectiveness in predicting diamond prices but with slightly higher errors compared to the RF regressor. The Boosted Decision Tree Regressor had an RMSE of 711.31 and an $$\text {R}^2$$ R 2 score of 0.968, demonstrating its ability to capture some of the underlying patterns but with higher errors than the RF and XGBoost models. In contrast, the KNN regressor yielded a higher RMSE of 1346.65 and a lower $$\text {R}^2$$ R 2 score of 0.887, indicating its inferior performance in accurately predicting diamond prices compared to the other models. Similarly, the Linear Regression model performed similarly to the KNN regressor, with an RMSE of 1395.41 and an $$\text {R}^2$$ R 2 score of 0.876. The Support Vector Regression model showed the highest RMSE of 3044.49 and the lowest $$\text {R}^2$$ R 2 score of 0.421, indicating its limited effectiveness in capturing the complex relationships in the data. Overall, the study demonstrates that the RF outperforms the other models in terms of accuracy and predictive power, as evidenced by its lowest RMSE, highest $$\text {R}^2$$ R 2 score, and perfect classification performance. This highlights its suitability for accurately predicting diamond prices. The study not only provides an effective tool for the diamond industry but also emphasizes the importance of considering both regression and classification approaches in developing accurate predictive models. The findings contribute valuable insights for pricing strategies, market trends, and decision-making processes in the diamond industry and related fields

Using integrated weighted survival difference for the two-sample censored data problem

For the two-sample censored data problem, Pepe and Fleming [Pepe, M.S., Fleming, T.R., 1989. Weighted Kaplan-Meier statistics: A class of distance tests for censored survival data. Biometrics 45, 497-507] introduced the weighted Kaplan-Meier (WKM) statistics. From these statistics we define stochastic processes which can be approximated by zero-mean martingales. Conditional distributions of the processes, given data, can be easily approximated through simulation techniques. Based on comparison of these processes, we construct a supremum test to assess the model adequacy. Monte Carlo simulations are conducted to evaluate and compare the size and power properties of the proposed test to the WKM and the log-rank tests. The procedures are illustrated using real data.

This methodological flowchart shows the approach used from data collection, preprocessing, modelling and presentation of results used.

This methodological flowchart shows the approach used from data collection, preprocessing, modelling and presentation of results used.</p

Scatterplot of relationships between mean annual maximum temperature and fire frequency.

Graphs show different relationship for each era suggesting the difference in time periods and effect of other variables.</p

Violin plot of the four model performance metrics in the simulation study.

Panel a shows the model results by bias on test data, Panel b shows the model results by MASE on test data, Panel c shows the model results by RMSE on training data and Panel d shows the model results by RMSE on test data.</p

Scatterplot of relationships between mean annual rainfall and fire frequency.

Graphs show different relationship for each era suggesting the difference in time periods and effect of other variables.</p

Summary of real world dataset variables (n = 218).

Summary of real world dataset variables (n = 218).</p

The actual fire counts, the predicted values and the prediction intervals of the NB and BNB models on testing data.

The actual fire counts, the predicted values and the prediction intervals of the NB and BNB models on testing data.</p

BNB model beta estimates and credible intervals on data.

BNB model beta estimates and credible intervals on data.</p