Data Mining and AI for Early Diagnosis and Treatment Optimization in Autoimmune Encephalitis

To guarantee high-quality and trustworthy data, we first highlight the need of preprocessing. The dataset is prepared for analysis by using standard methods such as data cleansing, normalization, and feature engineering. The technique relies heavily on the feature selection process, which picks out the most informative aspects of a large collection of clinical, imaging, and laboratory data. ReliefF (Relief Feature Selection), Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA) are three of the most popular feature selection techniques utilized here. These techniques make it easier to find unique characteristics in data, which in turn makes machine learning models more effective. A logistic regression model is used to predict the likelihood of autoimmune encephalitis based on certain characteristics to make an early diagnosis. In addition, linear programming is used to incorporate treatment optimization as an optimization issue. This guarantees that the suggested strategy maximizes therapy effectiveness within the bounds of possible side effects, available resources, and individual patient preferences. By adhering to a code of ethics, we preserve patient privacy and meet all applicable regulations. The suggested technique provides a holistic answer for autoimmune encephalitis evaluation and management. It greatly enhances diagnostic precision, therapeutic efficacy, and computational efficiency by merging data mining, AI, and mathematical modeling. These developments, together with our dedication to ethical compliance, make our method a very attractive and effective option for the diagnosis and individualized treatment of autoimmune encephalitis.