SMART HEALTHCARE DISEASE PREDICTION SYSTEM

INTRODUCTION

● It might have happened so many times that you or someone yours need doctors help immediately, but they are not available due to some reason. The Health Prediction system is an end user support and online consultation project. Here we propose a system that allows users to get instant guidance on their health issues through an intelligent health care system online. The system is fed with various symptoms and the disease/illness associated with those systems. The system allows user to share their symptoms and issues. It then processes user’s symptoms to check for various illnesses that could be associated with it. Here we use some intelligent data mining techniques to guess the most accurate illness that could be associated with patient’s symptoms.

PROBLEM DEFINITION

Prediction of health disease may seem tricky, but this is part of user service system (application support direct contact with user). The core idea behind the project is to propose a system that allows users to get instant guidance on their health issues. This system is fed with various symptoms and the disease/illness associated with those systems. This system allows user to share their symptoms and issues It then processes user’s symptoms to check for various illnesses that could be associated with it If the system is not able to provide suitable results, it informs the user about the type of disease or disorder it feels user’s symptoms are associated with and also suggest the doctor to whom he or she can contact.

PROJECT DESCRIPTION

To beat the downside of existing framework we have created smart health prediction System. We have built up a specialist framework called Smart Health Prediction framework, which is utilized for improving the task of specialists. A framework checks a patient at initial level and proposes the possible diseases. It begins with getting some information about manifestations to the patient, in the event that the framework can distinguish the fitting sickness, at that point it proposes a specialist accessible to the patient in the closest conceivable territory. On the off chance that the framework isn’t sufficiently sure, it asks few questions to the patients, still on the off chance that the framework isn’t sure; at that point it will show a few tests to the patient. In light of accessible total data, the framework will demonstrate the result. Here we utilize some intelligent methods to figure the most precise disorder that could be associated with patient’s appearances and dependent on the database of a couple of patient’s restorative record, calculation (Naïve Bayes) is connected for mapping the side effects with conceivable diseases. This framework improves undertaking of the specialists as well as helps the patients by giving vital help at a soonest organize conceivable.

Design

ASSUMPTIONS AND DEPENDENCIES

i. We are assuming that whatever the symptoms the patient has, it is present in the database.

ALGORITHM FOR DIESEASES PREDICTION

Start:

FUNCTIONAL REQUIREMENTS

Registration Process 

NON-FUNCTIONAL REQUIREMENTS

Security: 

SOFTWARE REQUIREMENTS:


Technology: Python Django 

REFERENCES

https://www.geeksforgeeks.org/python-django/ https://www.javatpoint.com https://www.python.org/ https://www.tutorialspoint/

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