Enhancing Cardiovascular Disease Diagnosis With Data-Driven Predictive Systems
TL;DR
The proposed approach uses a Quantum Neural Network for machine learning for machine learning in an intelligent Cardiovascular Disease (CVD) prediction system that has the highest sensitivity and specificity in the current literature, matching exact expert opinions.
Abstract
The proposed approach uses a Quantum Neural Network for machine learning in an intelligent Cardiovascular Disease (CVD) prediction system. The diagnosis of heart disease in the early stages is significant, but physicians do not always have enough time to go through the patient's historical data. This system improves medical care by quickly analysing patient records and generating risk predictions with high precision. Data was collected on 815 patients with heart disease symptoms for training and evaluation, and the Framingham study dataset of 5,209 patients was used for validation. Its accuracy rate is 98.5%, and it has the highest sensitivity and specificity in the current literature, matching exact expert opinions. Integrating this decision-support system in medical diagnostics can allow clinicians to personalise their treatment strategies, cutting expenses and enhancing clinical outcomes. This prognostic tool provides up-to-date knowledge and can be used in daily clinical practice to improve decision-making and increase treatment efficiency in cardiovascular medicine. The results validate its advantage over current prognostic systems.