Deep Generative Decision Neural Network Approach for Effective Early Prediction of Meninges

Meningitis is a contagious disease that leads to neurocognitive impairment. It is caused by inflammation in the protective membranes surrounding the brain and spinal cord and can be caused by various pathogens, such as viruses and bacteria. It is necessary to make predictions and classify meninges diseases. The conventional techniques for forecasting meninges are time-consuming and prone to errors, resulting in low accuracy rates with high false classifications. To tackle these problems, this study presents a Deep Learning (DL) method to aid in predicting meningitis. The novel introduces a Deep Generative Decision Neural Network (DGDN2) algorithm for predicting meninges. The Box-Plot Standardization (BPS) algorithm eliminates null and missing values to prepare the dataset. Next, the Mutual Gain Genetic Algorithm (MG2A) is utilized to select essential features of meninges diseases. Finally, the DGDN2 algorithm is used to predict meninges diseases efficiently. According to experimental results, the proposed model achieves higher accuracy, precision, recall, and F-measure than previous methods.