Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations. Therefore, the physicians find it difficult to understand these models and rarely trust them for clinical use. In this study, a carefully constructed, efficient, and interpretable diabetes detection method using an explainable AI has been proposed. The Pima Indian diabetes dataset was used, containing a total of 768 instances where 268 are diabetic, and 500 cases are non-diabetic with several diabetic attributes. Here, six machine learning algorithms (artificial neural network (ANN), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost, XGBoost) have been used along with an ensemble classifier to diagnose the diabetes disease. For each machine learning model, global and local explanations have been produced using the Shapley additive explanations (SHAP), which are represented in different types of graphs to help physicians in understanding the model predictions. The balanced accuracy of the developed weighted ensemble model was 90% with a F1 score of 89% using a five-fold cross-validation (CV). The median values were used for the imputation of the missing values and the synthetic minority oversampling technique (SMOTETomek) was used to balance the classes of the dataset. The proposed approach can improve the clinical understanding of a diabetes diagnosis and help in taking necessary action at the very early stages of the disease.
During the third wave of the coronavirus epidemic in Bangladesh, the death and infection rate due to this devastating virus has increased dramatically. The rapid spread of the virus is one of the reasons for this terrible condition. So, identifying the subsequent cases of coronavirus can be a great tool to reduce the mortality and infection rate. In this article, we used the autoregressive integrated moving average-ARIMA(8,1,7) model to estimate the expected daily number of COVID-19 cases in Bangladesh based on the data from April 20, 2021, to July 4, 2021. The ARIMA model showed the best results among the five executed models over Autoregressive Model (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Rolling Forest Origin. The findings of this article were used to anticipate a rise in daily cases for the next month in Bangladesh, which can help governments plan policies to prevent the spread of the virus. The forecasting outcome indicated that this new trend(named delta variant) in Bangladesh would continue increasing and might reach 18327 daily new cases within four weeks if strict rules and regulations are not applied to control the spread of COVID-19.
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