Smart Healthcare System (original) (raw)

Classical diagnosis is a process in which a medical practitioner examines the body of a patient for any possible signs or symptoms of a medical condition, undergo various medical examinations, and then conclude. This is often challenging because many signs and symptoms are nonspecific. Medical facilities must be advanced so that better decisions for patient diagnosis and treatment options can be created. Machine learning allows models to rapidly analyze data, and deliver results, leveraging both historical and real-time data. Healthcare service providers can make better decisions on patient's diagnoses and treatment options, which will end up in overall improvement of healthcare services and thereby increasing patient satisfaction. By this project, we are trying to implement functionalities of machine learning in healthcare within a single system. Some cases can occur when an early diagnosis of a disease is not within immediate reach. In such cases, this system can be effectively implemented. As widely said, "Prevention is best than cure", prediction of diseases and epidemic outbreak would lead to early prevention of an occurrence of a disease. This project mainly focuses on the development of a system, or we could say an immediate medical provision that would incorporate the symptoms and other medical data collected from the patient and store them into a Smart health dataset. This dataset would then be analyzed using the Naïve Bayesian machine learning algorithm to deliver results with maximum accuracy. GPS tracking will be used to suggest nearest doctor or specialist if the patient needs referral. This system will have the capacity to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life. Patients can seek information regarding diseases, pathological tests referred, get answer to any kind of question and solve any problem related to the disease. I.