Evaluating the Performance of Vascular Feature Extraction Boosting Algorithm for Peripheral Arterial Disease Detection

A common vascular disease called peripheral arterial disease (PAD) is characterized by constricted or blocked arteries in the legs, which lowers blood flow and raises the risk of consequences including cardiovascular events or amputation of a limb. Timely intervention and management of PAD are contingent upon early identification. Atherosclerosis, or plaque accumulation in the arteries, is the main cause of this illness. Therefore, leg pain, cramping, numbness, weakness, or coolness in the legs are possible signs of PAD, especially when engaging in physical activity. In addition to impairing mobility and quality of life, PAD raises the possibility of consequences like tissue damage, persistent sores, and even limb amputation if treatment is not received. Mixes of dietary adjustments, medication, and, in extreme situations, surgical procedures are used to manage PAD. A nutritious diet, frequent exercise, giving up smoking, and keeping a healthy weight are a few examples of lifestyle changes. It may be necessary to prescribe pharmaceuticals to decrease cholesterol, antiplatelet agents, blood flow improvers, and symptom managers. Procedures like angioplasty, stenting, or bypass surgery may be required in cases when PAD progresses and symptoms are severe to restore blood flow to the affected limbs. To avoid issues and enhance results, PAD must be identified early and managed effectively. As a result, those who are at risk such as those with diabetes, high blood pressure, high cholesterol, or a history of smoking should have screening for PAD regularly. All things considered, PAD is a dangerous illness that needs to be managed proactively to avoid complications and preserve a high standard of living. Through the management of risk factors, adoption of a healthy lifestyle, and adherence to an individualized treatment plan, people with PAD can effectively manage their illness and lower their chance of complications. For long-term treatment and best results, regular monitoring and follow-up with healthcare specialists are necessary. Vascular feature extraction boosting (VFEB), a hybrid algorithm that combines the strength of vascular feature extraction with boosting approaches, is the unique strategy for PAD identification that is present in this study. The VFEB approach makes use of gradient boosting machines (GBMs) to improve classification performance based on these features and convolutional neural networks (CNNs) to extract discriminative features from vascular imaging data. The goal of VFEB is to increase the precision and resilience of PAD detection by combining CNNs for feature extraction with GBMs for classification. Using a dataset of vascular imaging data, we assess the VFEB algorithm’s performance and compare it with other established techniques. The findings show that VFEB performs better than other methods at identifying PAD, providing encouraging opportunities for the early detection and treatment of this crippling vascular ailment. The VFEB system achieves an outstanding accuracy of 99.87%, an F1 Score of 99.64, and MCC 99%. Compared to the conventional methods, VFEB obtains improved accuracy, F1 Score, and MCC by integrating boosting techniques with vascular feature extraction. This study emphasizes the value of utilizing cutting-edge computational tools to improve vascular health outcomes and shows how hybrid algorithms, such as VFEB, may improve PAD identification.