Reinforcement of Feature Vector Learning for Predicting Cardiac Resynchronization Therapy Using Automatic Weighted Clustering

Unsupervised methods have become essential for managing and analyzing large datasets due to their ability to uncover underlying patterns without labeled data. In particular, unsupervised learning techniques have shown promise in enhancing the robustness of feature vectors, which can subsequently improve the accuracy and efficiency of classification models. This study presents a novel approach called Automatic Weighted Clustering (AWC), designed as an unsupervised algorithm specifically aimed at optimizing data segmentation to support and enhance the classification performance of supervised algorithms. The AWC algorithm, applied in the context of medical datasets, enables efficient knowledge discovery by automatically identifying and weighting important data features. To assess AWC’s performance, we conducted extensive experiments across various datasets to test the algorithm’s generalizability and scalability. The AWC approach yielded nine distinct clusters from the dataset, demonstrating a ۶.۲۶% accuracy improvement compared to the standard feature vector and a ۴.۸۳% increase over the traditional K-means clustering method. Additionally, AWC achieved a ۳۰.۶۸% reduction in execution time, highlighting its potential for faster, more accurate medical data analysis and classification.

کلیدواژه ها:

Unsupervised Learning ، Automatic Weighted Clustering ، Feature Vector Reinforcement ، CARDIAC RESYNCHRONIZATION THERAPY