Work

Research on features selection of medical diagnostic models based on L-S-ACO algorithm

Year: 2026

Type: article

Abstract: Feature selection for medical diagnostic models must account for feature interdependencies rather than independence. Ant colony optimization (ACO), a heuristic algorithm inspired by ant foraging, effectively addresses feature selection and correlation challenges. Published studies show that integrating ACO with other algorithms and refining pheromone-heuristic computations significantly boosts medical data feature selection performance. To tackle the issue of features correlation in medical diagnostic models, this study presents the L-S-ACO algorithm by combining the ACO algorithm with two additional algorithms: Light gradient boosting machine (LightGBM) and stochastic average gradient (SAG). Based on this algorithm, a medical diagnostic model is developed, and its practical application in disease diagnosis is analyzed. The analysis results demonstrate that the proposed algorithm improves accuracy (ACC) by 5.81%, F1 score by 6.21%, area under the curve (AUC) by 4.08%, precision by 5.12%, and recall by 6.35%.

Source: iScience

DOI: 10.1016/j.isci.2026.115005

Authors: Ping Wang, Boyuan Tan, Yansong Fu, LeiLei Zhu, Xiuli Sun, Wei Su

Institution: Changchun University of Chinese Medicine

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,240