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Disease Symptom Analysis Based Department Selection Using Machine Learning for Medical Treatment

Most of the patients today who face health problems, initially take advice from unprofessional or people with no knowledge that makes them more vulnerable. In many occasions, doctors also get confused with identifying actual disease. This might happen as they usually identify disease based on their limited experience. Moreover, general patient selects doctor according to their will and with no knowledge about the disease that may need specialist doctor. But some disease cannot be confirmed without a specialized doctor. Therefore, this paper proposes a Machine Learning based disease symptom analysis technique for assisting the patients seeking proper treatment by selecting accurate medical department using the symptom that they can easily recognize. Proposed framework will use machine learning technique to select a medical department based on the joint consideration of various disease symptoms of the patient. We investigate our proposed framework by using 9 different supervised machine learning techniques. Performance of framework for identifying appropriate medical department under the machine learning techniques is thoroughly investigated and compared. This framework can be used for telemedicine platform or in automated hospital management sector. This may create a path of enormous development in health care sector.