DETECTION & PREDICTION OF COMORBIDITIES OF DIABETES USING MACHINE LEARNING

Abstract

High blood sugar levels that persist for a long
time are the hallmark of diabetes, a metabolic
illness. A number of health problems, or co
morbidities, might develop as a result. These
can include things like neuropathy,
cardiovascular disease, renal disease, and
kidney failure. In order to intervene quickly
and enhance patient outcomes, it is essential to
detect and anticipate co morbidities in people
with diabetes. Clinical observations and
statistical evaluations of patient data were the
backbone of early attempts to manage diabetic
co morbidities. The intricacy and diversity of
co morbidity patterns were beyond the
capabilities of these techniques,
notwithstanding their value. A sea change
occurred in this field with the introduction of
machine learning, which allowed for the
creation of more complex and precise
prediction models. One challenge in using
machine learning to the detection and
prediction of diabetes co morbidities is the
need to use data from diabetes patients to
construct models that can determine the
probability of acquiring certain co morbidities.
This work necessitates the analysis of
numerous datasets in order to provide
healthcare professionals with meaningful
insights. These datasets may include patient
demographics, medical history, test findings,
and even genetic information. Healthcare
providers used to rely heavily on manual
analysis for diabetes co morbidity diagnosis
and prediction. Human subjectivity and the
inefficiency of processing enormous quantities
of data severely constrained this technique,
even if they depended on their knowledge and
experience. Machine learning models excel in
detecting complex patterns, which it failed to
do as well. Co morbidity diagnosis and
prediction in diabetic patients must be done
accurately and promptly. People with diabetes
have a much worse quality of life due to co
morbidities, and better treatment options are
available with earlier intervention. The
promise of machine learning lies in its ability
to sift through massive datasets in search of
hidden links and patterns that humans may
miss. More tailored and efficient healthcare
interventions may result from this. Massive
volumes of patient data may be analysed with
the help of ML algorithms to find patterns and
predictive variables linked to certain co
morbidities. The predicted accuracy of these
models may be enhanced over time as they
learn from past data, incorporate new
information, and adapt. In this setting,
machine learning's use is a huge step forward
in personalised medicine, with the ability to
alleviate co morbidities and make people's
lives with diabetes much better.