Human monkeypox diagnose (HMD) strategy based on data mining and artificial intelligence techniques

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In May 2022, monkeypox re-emerged as a rare zoonotic disease that is an important viral disease for public health. Monkeypox can be transmitted from animals to humans, between humans through close contact with an infected human, or with a virus stained substance. Through this paper, a new detection strategy based on artificial intelligence techniques is provided to early detect monkeypox patients. This strategy is called Human Monkeypox Detection (HMD) strategy and mainly consists of two main phases, which are; (i) Selection Phase (SP) and (ii) Detection Phase (DP). While SP tries to select the best features, DP tries to introduce fast and accurate detection based on valid data from SP. In SP, an Improved Binary Chimp Optimization (IBCO) algorithm as a new feature selection algorithm is introduced to select valuable features before learning an Ensemble Diagnosis (ED) model as a new diagnostic algorithm in the next phase called DP. In fact, the proposed IBCO algorithm is a hybrid selection algorithm that includes both filter and wrapper methods. IBCO consists of a filter layer called Filter Selection Layer (FSL) and a wrapper layer called Wrapper Selection Layer (WSL). At first, monkeypox dataset is entered into FSL to quickly select meaningful features by using ‘m’ filter selection techniques. Then, ‘m’ sets of selected features are fed into WSL to construct the initial population of Binary Chimp Optimization (BCO) algorithm to precisely choose the best set of features for the next phase (DP). Finally, the ED model will be correctly trained on the filtered data from FSL. This model consists of three diagnostic algorithms called Weighted Naïve Bayes (WNB), Weighted K-Nearest Neighbors (WKNN), and deep learning which are combined using a new weighted voting method to provide the best diagnostic results. The weighted values of WNB algorithm are determined by measuring the impact of each feature on the class categories while the Grey Wolf Optimization (GWO) algorithm is used to determine the weighted values of WKNN. Experimental results illustrated that the suggested feature selection algorithm called IBCO outperforms other modern feature selection methods and also the proposed ED model outperforms other modern diagnostic models. At the end, the HMD strategy gives the best results compared to other modern strategies with accuracy, precision, and recall values equal 98.48%, 91.1% and 88.91% respectively. Also, the HMD gives 92.56%,89.01%,88.01%,85.01%, 83.9%, and 5.4 s for micro-average precision, micro-average recall, macro-average precision, macro-average recall, F1-measure, and implementation time values respectively.