  Worldwide, several cases go undiagnosed due to poor healthcare support in
remote areas. In this context, a centralized system is needed for effective
monitoring and analysis of the medical records. A web-based patient diagnostic
system is a central platform to store the medical history and predict the
possible disease based on the current symptoms experienced by a patient to
ensure faster and accurate diagnosis. Early disease prediction can help the
users determine the severity of the disease and take quick action. The proposed
web-based disease prediction system utilizes machine learning based
classification techniques on a data set acquired from the National Centre of
Disease Control (NCDC). $K$-nearest neighbor (K-NN), random forest and naive
bayes classification approaches are utilized and an ensemble voting algorithm
is also proposed where each classifier is assigned weights dynamically based on
the prediction confidence. The proposed system is also equipped with a
recommendation scheme to recommend the type of tests based on the existing
symptoms of the patient, so that necessary precautions can be taken. A
centralized database ensures that the medical data is preserved and there is
transparency in the system. The tampering into the system is prevented by
giving the no "updation" rights once the diagnosis is created.
