Many patients miss their appointments all around the world and many of them don't even cancel at all or don't do so in time due to several reasons. In order to address the widespread issue of medical no-shows, this paper proposes a solution that involves building a machine learning model utilizing patient datasets that are already available. This model will identify patterns and links between various patient factors and the patients' propensity to miss appointments. As a result, based on their information, it is possible to anticipate the chance of a patient appearing. Based on the Support Vector Machines classification technique, the machine learning model created the solution predictive model. Effective healthcare services are vital in today's fast-paced environment. This strategy aims to reduce the distance between patients and medical professionals by offering a workable and friendly solution. For certain medical institutions, such as clinics and hospitals, this initiative makes it easier for patients and customers to schedule doctor appointments online. Using this technology, patients may easily browse a database of doctors' biographies, specializations, and availability. Even the day and time of their choosing can be chosen for appointments. Each patient's appointment request will be scheduled by this doctor's appointment system and forwarded to the physician. The system administrator will update the list of doctors, including their specialties, personal information, and system access credentials. Patients will look for a physician who specializes in their requirements by exploring the doctor's appointment system online. Before making their request, the patient can browse the doctor's weekly schedule to choose a day and time that works best for them. Following that, the physicians have access to all of their appointments as well as the patients' appointment requests, which are prioritized according to their availability. It gives medical professionals a strong tool for successfully managing the schedules, which reduces administrative strain and ensures a positive patient experience.
