An Optimized PSO-PNN Hybrid Model for Enhancing Diagnostic Accuracy in Cardiovascular Disease Prediction
Year: 2026
Type: article
Abstract: Cardiovascular Disease (CVD) is a prevailing issue across the world. It requires an accurate method for diagnosing in order to treat the disease effectively. The use of machine learning (ML) techniques has gained popularity for diagnosing CVDs. Some existing ML models have demonstrated good results using the process of manual hyperparameter tuning. But manual hyperparameter tuning is a very tedious task that may cause fluctuations in diagnosis accuracy. To construct a PSO-tuned classifier that provides stable diagnosis for CVD, this paper proposes a robust hybrid framework named PSO-PNN using a particle swarm optimization algorithm for fine-tuning the value of the Smoothing Parameter (σ) of a probabilistic neural network (PNN). PSO-PNN could be employed as an effective classifier for cardiovascular disease. To validate the effectiveness of PSO-PNN, a dataset obtained from UCI Cleveland database was utilized containing 303 patients’ data. The results indicated that the proposed PSO-PNN framework improved the baseline PNN performance; obtained an accuracy of 91.3%, an ROC-AUC value of 94.8%, and a recall rate of 95.24%; and showed stable predictive performance across 30 independent PSO executions. Additionally, SHAP was applied as a post hoc explainability method to interpret the trained PSO-PNN predictions and identify the contribution of input features. The proposed framework may support future decision-support applications for CVD prediction; however, further external validation is required before practical clinical use can be considered.
Source: Algorithms
DOI: 10.3390/a19080705
Institution: Northern Border University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7204105822