Abstract:
Despite significant strides in modern technology, a substantial portion of the global
population is still not getting proper medical care. This issue is particularly pronounced in
developing countries which face a double burden of communicable and non-communicable
diseases. On the other hand, limited access to quality healthcare, particularly in remote
areas where skilled doctors may not be available, is a significant challenge for patients to
receive appropriate treatment. To solve this problem, this project will be able to make the
treatment much easier and more accurate. The system is designed to enhance the accuracy,
reliability, and efficiency of disease prediction by leveraging the power of machine
learning algorithms and reliable datasets. It will also make the work of doctors a lot easier
because this project can diagnose possible diseases and give suggestions about what
restrictions should be followed at home to get rid of these diseases. Since disease prediction
is a very crucialsubject where accuracy has to be maximized, ten machine-learning models,
including Decision Trees, Random forest, Bagging Classifier, Support Vector Machine and
AdaBoostClassifier and hybrid (a combination of several machine-learning) models have
been used in this project that capitalize on the strengths of models or data types while
mitigating their respective weaknesses.
The fundamental objective of this work is to significantly enhance the user experience of
the existing disease prediction system. This is achieved through the design and
implementation of a user-friendly interface using a Python framework "Streamlit" that
facilitates not only disease prediction but also recommends appropriate doctors and
suggests preventative measures
