Data Mining for Predictive Analytics and Optimization of Treatment Plans in Cardiovascular Disease Management using Neural Networks

Abstract

This section presents an integrated method that merges data mining techniques and neural networks to augment predictive analytics and optimize treatment strategies for managing cardiovascular diseases. Algorithm 1 focuses on data preprocessing and feature selection, essential for refining the patient data. It standardizes the input data by normalization and selects critical features using information gain, enhancing the subsequent neural network's efficiency in predicting disease progression. Algorithm elucidates the training of a neural network, utilizing the pre-processed data and disease progression labels. The neural network undergoes iterative training to minimize the loss function, resulting in a trained model capable of accurate predictions. Algorithm emphasizes the utilization of the trained neural network to optimize personalized treatment plans for cardiovascular disease management. By predicting disease progression and evaluating treatment options, it tailors' treatment plans to individual patient needs, thereby enhancing the effectiveness of healthcare interventions. The proposed method is demonstrated to surpass traditional techniques such as Linear Regression, Decision Trees, Support Vector Machines, K-Nearest Neighbors, Random Forest, and Logistic Regression in terms of critical performance metrics. Leveraging the power of neural networks, the proposed method showcases superior accuracy, precision, recall, Fl score, and AUC-ROC. Moreover, a comprehensive evaluation involving sensitivity, specificity, Matthews Correlation Coefficient (MCC), Negative Predictive Value (NPV), Youden's Index, and Cohen's Kappa consistently emphasizes its advanced performance, validating its potential to revolutionize cardiovascular disease management.