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In this research, we consider four seasonal diseases such as dengue, malaria, typhoid, and pneumonia. In this research, the researcher finds the dangerous symptoms of victims based on their age group. The real-time dataset was used in this study. The dataset for this study was gathered from hospitals in the Madurai district between 2019 and 2020. Feature selection is the prime element of patient risk recognition. It is used to pick the most accurate attributes for prediction. The dataset is divided into 70% training data and 30% testing data. This research proposes the feature selection method Boruta-XGBoost for improving accuracy. In this research, we discuss various attribute selection algorithms, including the Boruta algorithm, the XGBoost algorithm, the recursive feature elimination method (RFE), and the PRF-BXGBoost (Patient Risk Factor-Boruta-XGBoost) algorithm. The proposed method provides greater accuracy when compared to other variable selection methods. 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