Authors :
Pramodd Komarneni; Toshan Kumar Kalakoti; Pavan Kumar Narla; Sai Pujitha Alla; Richitha Bomma
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/y83tkcu6
Scribd :
https://tinyurl.com/yk6h8nfx
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR1994
Abstract :
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.
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.
