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.

● In doctor module when doctor login to the system doctor can view his patient details and the report of that patient. Doctor can view details about the patient search what patient searched for according to their prediction. Doctor can view his personal details. Admin can add new disease details by specifying the type and symptoms of the disease into the database. Based on the name of the disease and symptom the datamining algorithm works. Admin can view various disease and symptoms stored in database. This system will provide proper guidance when the user specifies the symptoms of his illness.

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.

ii. The patient has a high-speed internet.

iii. We are assuming that the patient is able to identify his/her symptoms clearly.

ALGORITHM FOR DIESEASES PREDICTION

Start:

Step 1: Request the user for a symptom as input.

Step 2: Check the input symptom against list of symptoms in database for validity.

Step 3: If input symptom is not a valid symptom, prompt the user with “Invalid symptom” and go to step 1.

Step 4: Use the finalized input symptom from step 3 to find possible diseases.

Step 5: If possible, disease is only one then present the user with that disease and corresponding doctor.

Step 6: Else Take union of sets of symptoms of each disease found in step 4.

Step 7: If set generated in step 6 is empty then present the user with multiple possible disease list generated in step 4 and corresponding doctor(s).

Step 8: Else Ask the user to select one more symptom from the set of symptoms generated in step 6.

Step 9: Go to step 4 with symptom selected by user in step 8 as input symptom.

NOTE: Maximum times Loop from step 4 to 9 must not execute more than 9 times.

FUNCTIONAL REQUIREMENTS

Registration Process 

Adding Patients: The system enables the patients to register themselves as patient. 

Adding Doctors: The system enables the doctors to register and sign up with their specialty as doctors. Prediction of Diseases 

Symptoms of Diseases: Every patient has to enter the symptoms they are feeling and system will predict the results and help the patient to contact the doctor according to disease. 

Login Module for doctors and patients.

NON-FUNCTIONAL REQUIREMENTS

Security: 

Logon ID: Any users who make use of the system need to hold a Logon ID and password. 

Modifications: Any modifications like insert, delete, update, etc. for the database can be synchronized quickly and executed only by the site administrator.

Response Time: The system provides acknowledgment in very fast once the patient’s symptoms are checked. 

Capacity: The system needs to support many users.

Reliability: The system needs to available all the time.

SOFTWARE REQUIREMENTS:


Technology: Python Django 

IDE: VS Code/Atom 

Client Side Technologies: HTML, CSS, JavaScript

Server Side Technologies: Python 

Data Base Server: SQLite

Operating System: Microsoft Windows/Linux

REFERENCES

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

Team Members:-

Akshit Kumar(ENG18CS0029)

Anirban Saha(ENG18CS0037)

Arjun Upadhayay(ENG18CS0045)

Kanishk Raj(ENG18CS0124)

Kinshuk Kishore(ENG18CS0135)