ABSTRACT
Health care practices involve collecting all kinds of patient data which would help the doctor
correctly diagnose the condition the subject is likely suffering from. This data could be
everything from the simple symptoms observed by the subject, initial diagnosis by a
physician or a detailed test result from a lab. Thus far this data is only utilized for subjective
analysis by a doctor who then ascertains the disease in play using his/her personal medical
expertise.
We posit that there is definite potential for application of data mining routines on this
rich reserve of patient data. Employing apposite data mining techniques, useful patterns and
conclusions could be drawn from the raw data at our disposal. These findings could in turn be
utilized in a number of productive ways like to carry out automated diagnosis, equip doctors
with a better understanding of the causes and factors in play behind a particular disease.
We studied different data mining techniques like Logistic Regression, KNN, SVM,
Naïve Bayes, Decision trees and Random Forest and found out that KNN was giving the
highest accuracy of 78.57%. Automated diagnosis in particular could prove very useful for
the determination of non-critical diseases or in cases where a doctor may not be available to
carry out diagnosis such as in case of being in a remote location. We also aim to incorporate it
in an application which will provide diagnosis, storing of patient information and provide
visualization of that data.
