A powerful Peripheral Arterial Disease detection using machine learning-based severity level classification model and hyper parameter optimization methods

Abstract

This study addresses the critical need for improved detection and assessment of Peripheral Arterial Disease (PAD) using Artificial Intelligence (AI) algorithms, aiming to reduce global mortality rates associated with this condition. The research focuses on enhancing the efficiency of machine learning (ML) models for PAD detection and severity categorization by employing optimization techniques. Notably, this study introduces the application of Synthetic Minority Oversampling Technique (SMOTE) to mitigate imbalanced data challenges in PAD detection. The primary objective of this research is to develop highly efficient ML models utilizing six distinct AI classifiers and Hyper Parameter Optimization (HPO), specifically targeting PAD identification and severity level categorization. Leveraging the Cleveland and Statlog datasets, the study constructs and evaluates these models using all relevant features. The principal findings indicate remarkable advancements in PAD detection accuracy. The proposed models, optimized using SMOTE and Enhanced Decision Tree (EDT) through HPO, surpass state-of-the-art methodologies, achieving exceptional accuracies of 99.20% and 98.52% on the Cleveland and Statlog datasets, respectively. These models outperform existing approaches, assessed using comprehensive metrics such as F1 score, Accuracy, Precision, Recall, Specificity, and MCC. In conclusion, this study's innovative approach significantly improves the capability to assess PAD and categorize its severity levels accurately. The proposed models hold promise in aiding physicians to evaluate patients' cardiac conditions effectively, enabling early interventions that have the potential to mitigate mortality rates associated with cardiovascular diseases.