Computer-aided Disease Classification Based on Kplskelm Algorithm
Year: 2025
Type: article
Abstract: Objective: This study aims to explore the use of machine learning algorithms for predicting disease classification. Methods: An integrated algorithm (KPLSKELM) was proposed in this study. The algorithm employed kernel principal component analysis to transform the original data into a high-dimensional feature space, thereby enhancing its linear separability. It used the sparrow search algorithm (SSA) to optimize the weight matrix and parameters of the kernel extreme learning machine (KELM). The algorithm incorporated a Gaussian perturbation search mechanism to refine the population initialization strategy so as to mitigate the issues of poor convergence rate and susceptibility to local optima in the later SSA iterations. Lévy flight perturbations were introduced during the foraging search process of the sparrow population to guide the population in moving appropriate step sizes, thereby increasing the diversity of the spatial search. The proposed method was experimentally validated using a binary classification breast cancer dataset collected by Dr. William H. Wolberg from a Wisconsin hospital in the United States and a multiclass classification dataset of electrocardiographic recordings during childbirth. Multiple metrics were adopted to evaluate the classification performance. Results: The accuracy and F1_score of the KELM model remained relatively low across different percentages of the training set, although a recall of 1.0000 was consistently achieved. Both the SSA-improved KELM and the Lévy-improved SSA-optimized KELM algorithms exhibited better performance in terms of the comprehensive metric F1_score and improved with the increase in the percentage of the training set. The KPLSKELM model outperformed others in all metrics, with accuracy, precision, recall, and F1_score approaching or reaching the highest levels when using 90% of the training set. Conclusions: The proposed method demonstrated excellent performance in various disease prediction tasks, holding high practical application value. It provided a reference for further assisting clinicians in making more precise treatment decisions.
Authors: YIFENG DOU, JIANTAO LIU
Institution: Tianjin Medical University
Language: English
Topic: Machine Learning and ELM
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407239484