Smart Heart Attack Prediction With Online Appointment Booking System

Abstract

Cancer claims many lives globally annually. This frequently happens due to delayed detection of symptoms and inadequate availability of medical services. Modern medical practices often segregate risk evaluation and schedule management tasks independently, potentially causing significant delays in treating heart-related crises. The document presents an integrated strategy combining heart attack risk assessment with scheduling appointments via internet technology. By employing machine learning algorithms, this program analyzes data such as patients' ages, genders, blood pressures, heart rates, cholesterol counts, and sugar levels in their bloodstream to determine their likelihood of experiencing a heart attack. When detecting a critical situation, the software locates local experts immediately and arranges for a prompt consultation. Combining predictive analytics with intelligent scheduling enhances responsiveness in healthcare, shortens treatment durations, and improves patient results. Evidence indicates enhanced predictive precision and superior crisis response capabilities through AI-assisted medical systems. This highlights significant advantages in utilizing artificial intelligence for healthcare efficiency.