Predict Early Pneumonitis in Health Care Using Hybrid Model Algorithms

Abstract

Earlier methods concentrated on constructing a single CNN model, while the ensemble learning paradigm has received less attention. Based on our survey results, we chose to utilize an ensemble model comprising multiple CNN models to predict pneumonia diagnosis from x-rays. We proposed employing hybrid model algorithms to predict early pneumonitis in health care. Hybrid model algorithms are increasingly used in healthcare due to their ability to combine the strengths of multiple algorithms to achieve better performance than a single algorithm. In the case of early detection of pneumonitis, this is particularly important, as it is a serious condition that requires prompt diagnosis and treatment to prevent further complications. Artificial neural networks are well suited to process complex and non-linear data, which is important in healthcare where patient data can be highly heterogeneous. Decision trees can identify the most important features for predicting pneumonitis and can be used to generate rules for clinical decision-making. Support vector machines can be used for classification tasks, which is important in identifying patients who are at high risk for pneumonitis. By combining these algorithms in a hybrid model, it may be possible to achieve better performance than using a single algorithm alone. For example, an artificial neural network could be used to pre-process the data and identify the most important features, which are then fed into a decision tree for rule generation. The resulting rules could then be used to classify patients using a support vector machine.