Efficient Prediction of Seasonal Infectious Diseases Using Hybrid Machine Learning Algorithms with Feature Selection Techniques

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Abstract
Early seasonal disease risk identification is the most challenging task in the medical industry. The capacity to detect patients at risk of improvement throughout their hospital stay is critical for effective patient allocation and care among patients with seasonal diseases. Patient risk factor prediction is the most important issue in reducing victim mortality. Machine learning plays a vital role in identifying the risk level of patients. 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. The time complexity of the proposed method is low when compared to other algorithms.

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