Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor’s perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and decision making for CVD patients. Simple random sampling was used to select heart disease patients from the Khyber Teaching Hospital and Lady Reading Hospital, Pakistan. ML methods such as decision tree (DT), random forest (RF), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) were implemented for classification and prediction purposes for CVD patients in Pakistan. We performed exploratory analysis and experimental output analysis for all algorithms. We also estimated the confusion matrix and recursive operating characteristic curve for all algorithms. The performance of the proposed ML algorithm was estimated using numerous conditions to recognize the best suitable machine learning algorithm in the class of models. The RF algorithm had the highest accuracy of prediction, sensitivity, and recursive operative characteristic curve of 85.01%, 92.11%, and 87.73%, respectively, for CVD. It also had the least specificity and misclassification errors of 43.48% and 8.70%, respectively, for CVD. These results indicated that the RF algorithm is the most appropriate algorithm for CVD classification and prediction. Our proposed model can be implemented in all settings worldwide in the health sector for disease classification and prediction.

Objectives: The rising of new cases and death counts from the mpox virus (MPV) is alarming. In order to mitigate the impact of the MPV it is essential to have information of the virus's future position using more precise time series and stochastic models. In this present study, a hybrid forecasting system has been developed for new cases and death counts for MPV infection using the world daily cumulative confirmed and death series. Methods: The original cumulative series was decomposed into new two subseries, such as a trend component and a stochastic series using the Hodrick–Prescott filter. To assess the efficacy of the proposed models, a comparative analysis with several widely recognized benchmark models, including auto-regressive (AR) model, auto-regressive moving average (ARMA) model, non-parametric auto-regressive (NPAR) model and artificial neural network (ANN), was performed. Results: The introduction of two novel hybrid models, [ ] and [ ], which demonstrated superior performance compared to all other models, as evidenced by their remarkable results in key performance indicators such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), is a significant advancement in disease prediction. Conclusion: The new models developed can be implemented in forecasting other diseases in the future. To address the current situation effectively, governments and stakeholders must implement significant changes to ensure strict adherence to standard operating procedures (SOPs) by the public. Given the anticipated continuation of increasing trends in the coming days, these measures are essential for mitigating the impact of the outbreak.

Pakistan is considered among the top five countries with the highest CO₂ emissions globally. This calls for pragmatic policy implementation by all stakeholders to bring finality to this alarming situation since it contributes greatly to global warming, thereby leading to climate change. This study is an attempt to make a comparative analysis of the linear time series models with nonlinear time series models to study CO₂ emission data in Pakistan. These linear and nonlinear time series models were used to model and forecast future values of CO₂ emissions for a short period. To assess and select the best model among these linear and nonlinear time series models, we used the root mean square error (RMSE) and the mean absolute error (MAE) as performance indicators. The outputs showed that the nonlinear machine learning models are the best among all other models, having the lowest RMSE and MAE values. Based on the forecasted value of the nonlinear machine learning neural network autoregressive model, Pakistans CO₂ emissions will be 1.048 metric tons per capita by 2028. The increasing trend in emissions is a frightening and clear warning, suggesting that innovative policies must be initiated to reduce the trend. We encourage the Pakistan government to price CO₂ emissions by companies and entities per ton, adapt electricity production from hydro, wind, and different sources with no emissions of CO₂, initiate rigorous planting of more trees in the populated areas of Pakistan as forest covers, provide incentives to companies, organisations, institutions, and households to come out with clean technologies or use technologies with no CO₂ emissions or those with lower ones, and fund more studies to develop clean and innovative technologies with less or no CO₂ emissions.

This study examined the consequences of changes in minimum temperature, maximum temperature, relative humidity, and rainfall on the yields of maize, cassava, and yam per hectare of land in the Ashanti Region of Ghana. Correlation analysis of each climatic condition on the yield of each crop per hectare of land revealed that each of the climatic conditions was significant in predicting the crop yields. Separate multiple linear regression models were obtained for crop yield per hectare of land under all the climatic conditions. The regression models showed that an increase in maximum temperature reduces the yield of all the crops, whereas an increase in minimum temperature reduces only the yield of maize. Increases in relative humidity reduce the yield of maize alone, while increases in rainfall reduce the yield of only cassava. The significant multiple linear regression model for each crop yield indicated that 63.8% of the variations in the yield of maize per hectare of land, 74.3% of the variations in the yield of cassava per hectare of land, and 64.2% of the variations in the yield of yam per hectare of land are accounted for by minimum temperature, maximum temperature, relative humidity, and rainfall. We encourage the Government of Ghana, the Ministry of Food and Agriculture, and all stakeholders in the agriculture sector to increase their campaign on the consequences of climate change on the yield of these crops. They should educate farmers on the effects of overreliance on rainfed and traditional agricultural methods, introduce them to modern methods of agriculture, and provide them with varieties of these crops with higher-yielding capacities in higher temperatures.

The coronavirus disease 2019 (COVID-19) pandemic continues to destroy human life around the world. Almost every country throughout the globe suffered from this pandemic, forcing various governments to apply different restrictions to reduce its impact. In this study, we compare different time-series models with the neural network autoregressive model (NNAR). The study used COVID-19 data in Pakistan from February 26, 2020, to February 18, 2022, as a training and testing data set for modeling. Different models were applied and estimated on the training data set, and these models were assessed on the testing data set. Based on the mean absolute scaled error (MAE) and root mean square error (RMSE) for the training and testing data sets, the NNAR model outperformed the autoregressive integrated moving average (ARIMA) model and other competing models indicating that the NNAR model is the most appropriate for forecasting. Forecasts from the NNAR model showed that the cumulative confirmed COVID-19 cases will be 1,597,180 and cumulative confirmed COVID-19 deaths will be 32,628 on April 18, 2022. We encourage the Pakistan Government to boost its immunization policy.