Data Mining and AI Approaches for Early Detection and Management of Gastrointestinal Disorders

This study presents an innovative integration of Data Mining and Artificial Intelligence (AI) to advance early detection and management of gastrointestinal disorders. The methodology encompasses data preprocessing, feature selection, model development, and continuous monitoring for timely intervention. Preprocessing involves standardization and normalization to ensure consistent feature scaling and distribution. Addressing missing values and outliers is achieved using suitable methods such as mean imputation and robust statistical techniques. Feature selection methods, including Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA), aid in identifying the most informative features associated with gastrointestinal disorders. Support Vector Machines (SVM) excels in binary classification, distinguishing healthy individuals from those with gastrointestinal disorders. Deep Neural Network (DNN) is employed for non-linear feature representation and classification. In conclusion, the proposed method showcased superior performance in critical parameters when compared to traditional methodologies. Its enhanced accuracy, sensitivity, specificity, and computational efficiency position it as a promising approach in the domain of Data Mining and AI for early detection and management of gastrointestinal disorders. This innovation holds potential for significant advancements, promising improved healthcare outcomes and better lives for individuals suffering from gastrointestinal disorders.