In the modern world, chronic kidney disease is one of the most severe diseases that negatively affects human life. It is becoming a growing problem in both developed and underdeveloped countries. An accurate and timely diagnosis of chronic kidney disease is vital in preventing and treating kidney failure. The diagnosis of chronic kidney disease through history has been considered unreliable in many respects. To classify healthy people and people with chronic kidney disease, non-invasive methods like machine learning models are reliable and efficient. In our current work, we predict chronic kidney disease using different machine learning models, including logistic, probit, random forest, decision tree, k-nearest neighbor, and support vector machine with four kernel functions (linear, Laplacian, Bessel, and radial basis kernels). The dataset is a record taken as a case–control study containing chronic kidney disease patients from district Buner, Khyber Pakhtunkhwa, Pakistan. To compare the models in terms of classification and accuracy, we calculated different performance measures, including accuracy, Brier score, sensitivity, Youdent, specificity, and F1 score. The Diebold and Mariano test of comparable prediction accuracy was also conducted to determine whether there is a substantial difference in the accuracy measures of different predictive models. As confirmed by the results, the support vector machine with the Laplace kernel function outperforms all other models, while the random forest is competitive.

In the modern world, new technologies such as artificial intelligence, machine learning, and big data are essential to support healthcare surveillance systems, especially for monitoring confirmed cases of monkeypox. The statistics of infected and uninfected people worldwide contribute to the growing number of publicly available datasets that can be used to predict early-stage confirmed cases of monkeypox through machine-learning models. Thus, this paper proposes a novel filtering and combination technique for accurate short-term forecasts of infected monkeypox cases. To this end, we first filter the original time series of the cumulative confirmed cases into two new subseries: the long-term trend series and residual series, using the two proposed and one benchmark filter. Then, we predict the filtered subseries using five standard machine learning models and all their possible combination models. Hence, we combine individual forecasting models directly to obtain a final forecast for newly infected cases one day ahead. Four mean errors and a statistical test are performed to verify the proposed methodology’s performance. The experimental results show the efficiency and accuracy of the proposed forecasting methodology. To prove the superiority of the proposed approach, four different time series and five different machine learning models were included as benchmarks. The results of this comparison confirmed the dominance of the proposed method. Finally, based on the best combination model, we achieved a forecast of fourteen days (two weeks). This can help to understand the spread and lead to an understanding of the risk, which can be utilized to prevent further spread and enable timely and effective treatment.