Boosting medical diagnostics with a novel gradient-based sample selection method

• Published in: Computers in biology and medicine Vol. 182; p. 109165
• Main Author: Aymaz, Samet
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.11.2024; Elsevier Limited
• Subjects: Accuracy; Adaptive sampling; Algorithms; Artificial intelligence; Breast cancer; Breast Neoplasms - diagnosis; Cardiovascular disease; Classification; Cleveland dataset; Coronary artery disease; Coronary Artery Disease - diagnosis; Coronary Artery Disease - diagnostic imaging; Data processing; Databases, Factual; Datasets; Decision making; Diagnosis, Computer-Assisted - methods; Diagnostic systems; Effectiveness; Electronic health records; Feature selection; Female; Heart diseases; Humans; Internal Medicine; Machine learning; Medical diagnosis; Medical innovations; Medical personnel; Medical research; Methods; Neural networks; Optimization algorithms; Other; Particle swarm optimization; Regression analysis; Sample selection; Success; Support Vector Machine; Support vector machines; WDBC dataset; Breast cancer; Cleveland dataset; Sample selection; WDBC dataset; Coronary artery disease
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.109165

In the rapidly expanding landscape of medical data, the need for innovative approaches to maximize classification performance has become increasingly critical. As data volumes grow, ensuring that diagnostic systems work with accurate and relevant data is paramount for effective and generalizable classification. This study introduces a novel gradient-based sample selection method, the first of its kind in the literature, specifically designed to enhance classification accuracy by removing redundant and non-informative data. Unlike traditional methods that focus solely on feature selection, this approach integrates an advanced sample selection technique to optimize the input data, leading to more accurate and efficient diagnostics. The method is validated on multiple disease datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset and the Cleveland Coronary Artery Disease (CAD) dataset, demonstrating its broad applicability and effectiveness. To address dataset imbalance, the Adaptive Synthetic Sampling (ADASYN) method is employed, followed by Particle Swarm Optimization (PSO) for feature selection. The refined datasets are then classified using a Support Vector Machine (SVM), showing that even traditional classifiers can achieve substantial improvements when enhanced with advanced sample selection. The results underscore the critical importance of precise sample selection in boosting classification performance, setting a new standard for computer-aided diagnostics and paving the way for future innovations in handling large and complex medical datasets. •Introduced gradient-based sample selection to reduce computational load.•Combined ADASYN, PSO, and SVM for classification in a robust approach.•Used Decision Tree methods for objective function selection in PSO.•Demonstrated effectiveness on WDBC and Cleveland datasets.•Suggested future research: advanced methods and real-time application.