1. Smart Health Prediction System
Project Supervisor :Dr.Badar Sami
Internal Supervisor :Dr.Tehseen Ahmed Jilani

2. Team Introduction Amna Khan (Ep#1249006) amna_khan06@hotmail.com
Areeba Jabeen (Ep# )
Bushra Mansoor (EP# )
Muhammad Faizan Khan(EP# )

3. Why This ?
It might have happened so many times that you or someone yours need doctors help immediately, but they are not available due to some reasons or also sometimes it happens that we could not find the correct doctor for the treatment so to solve this problem we will implement the online intelligent Smart Health prediction web based application that will facilitate the patient to get instant guidance on their health issues

4. Project Summery
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.

5. Software Requirements
Windows 7 and above
Mysql server
Html
Php
Jquery
Xamp Server

6. Hardware Requirement Processor – Dual Core Hard Disk – 50 GB
Memory – 1GB RAM

7. Modules
Admin Module
Admin Login: Admin can login to the system using his ID and Password.
Add Doctor: Admin can add new doctor details into the database.
Add Disease: Admin can add disease details along with symptoms and type.
View Doctor: Admin can view various Doctors along with their personal details.
View Disease: Admin can view various diseases details stored in database.
View Patient: Admin can view various patient details who had accessed the system

8. Some Screen shots Of Admin Module

9. Patient Module
Patient Login: Patient can Login to the system using his ID and Password.
Patient Registration: If Patient is a new user he will enter his personal details and he will have user Id and password through which he can login to the system.
My Details: Patient can view his personal details.
Edit Patient Record : Patient can Edit his personal details.
Disease Prediction: - Patient will specify the symptoms caused due to his illness. System will ask certain question regarding his illness and predict the disease based on the symptoms and also suggest doctors based on the disease.
Search Doctor: Patient can search for doctor by specifying name or type.
My stuff : Patient also have the facility to manage their reports .

10. Some Screen Shots of Patient Module

11. Doctor Login: Doctor Login to the system using his ID and Password.
Doctor Module
Doctor Login: Doctor Login to the system using his ID and Password.
Doctor Registration: doctor can register them selves in the system
My Details: Doctor can view his personal details.
Edit Patient Record : Doctor can Edit his personal details.
My stuff : Doctor also have the facility to manage their files reports or any thing

12. Some Screen shots Of Doctor Module

13. Data Collection

17. Naive bayes algorithm
Naive Bayes algorithm is a classification algorithm based on Bayes’ theorems use in predictive modeling and this algorithm uses Bayesian techniques .This algorithm is less computationally intense then other and therefore is useful for quickly generating mining models to discover relationships between input columns and predictable columns.

18. Data required for naive bayes models
requirements for a Naive Bayes model
A single key column   Each model must contain one numeric or text column that uniquely identifies each record. Compound keys are not allowed.
Input columns   In a Naive Bayes model, all columns must be either discrete or discretized columns it is also important to ensure that the input attributes are independent of each other
At least one predictable column    The predictable attribute must contain discrete or discretized values. The values of the predictable column can be treated as inputs

19. Viewing the Model :To explore the model we can use the Microsoft Naive Bayes Viewer. The viewer shows you how the input attributes relate t
Making predictions 
After the model has been trained, the results are stored as a set of patterns, which we use to make predictions.
We can create queries to return predictions about how new data relates to the predictable attribute.

20. Remarks
Supports the use of Predictive Model Markup Language (PMML) to create mining models.
Supports drill through.
Does not support the creation of data mining dimensions.
Supports the use of OLAP mining models.

21. Database (ERD)
Data base on which we are working is Relational database

22. DFD

23. Use Case Diagram

24. References
modules/
cation/Na%C3%AFve_Bayes
types/start/