SMART HEALTH PREDICTION

1. JSS Academy of Technical Education
NOIDA
A
Report
On
“SMART HEALTH PREDICTION”
towards partial fulfillment of
Bachelor of Technology
In
Information Technology
Information Retrival and Management
Code : NIT061
Submitted to :
Ms. Gunjan Ansari
(Professor)
Submitted by :
Arhind Gautam (1509113029)
Akarsh Gupta (1509113011)
Apoorva Sonkar(1509113026)
Akshita Rana ( 1509113012 )
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2. TABLE OF CONTENTS
1. Introduction ...................................................................................................................3
2. Approach .......................................................................................................................4
3. Data set ...........................................................................................................................5
4. Methodology ..................................................................................................................6
5.Working of the system ...................................................................................................8
6. Code ...............................................................................................................................9
7. Experimental results .....................................................................................................11
8. Performance analysis ....................................................................................................13
9. Conclusion ....................................................................................................................14
10. References ....................................................................................................................15
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3. INTRODUCTION
Health is one of the most important assets of our life which directly reflects in any form of
progress or development. In today’s hustle and bustle of life, most of the people neglect this asset
which may be due to lack of time and complexity in the vast data available over the Internet.
Data mining has introduced various techniques which would overcome this problem and assist
us to emphasize on both health and work simultaneously. In present era, Data Mining is
becoming popular in healthcare field because there is a need of efficient analytical methodology
for detecting unknown and valuable information in health data. In health industry, Data Mining
provides several benefits such as detection of the fraud in health insurance, availability of
medical solution to the patients at lower cost, detection of causes of diseases and identification of
medical treatment methods. It also helps the healthcare researchers for making efficient health
care policies, constructing drug recommendation systems, developing health profiles of
individuals etc.
Information technologies are being increasingly implemented in healthcare organizations in
order to respond to the needs of doctors in their daily decision making activities. These models
can be called predictive, and they are being integrated into information system hospitals as a
model of decision making. In addition, it happens so many times that one may need a doctor’s
help immediately but it isn’t available due to certain reasons. That’s where Smart Health
Prediction steps in and is of first hand help to diagnose a patient and suggest health remedies.
The health prediction system is an end user support and online consultation project. Here, we
propose a system that allows user to get instant guidance on their health issues through an
intelligent health care system online. The system is fed with various symptoms and various
diseases/illness associated with those symptoms. The system allows user to share their symptoms
and issues. It then processes user symptoms to check for Body Mass Index(BMI), blood pressure
and various diseases associated with the symptoms of the user. If user symptoms matches any
disease in the database, it shows the probable disease user could have, recommended exercises
and diet chart.
Software Requirement :
Linux OS
Browser
Database MySQL
Adminer tool for database management
Apache Server 2.0
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4. APPROACH
There are several categories formed using data fed in database using blood pressure measured,
Body Mass Index(BMI), height, weight, gender, age, body status and output disease.
INPUT PAGE : Basically the whole project is divided into input, output and database. The input
part collects data like name of user, age, height, weight, blood pressure, gender, etc from user.
Inputs are taken by developer using a page. This page referred as input page, form page or
register page.
LOGICAL PAGE : This page is used for entering logic in the model. This page collects the
entered data and saved it in database. This page is also worked for retrieving the data from
database.
DATABASE : The only database is to save the data coming from logical page. It has columns
and rows for saving and efficiently storing the data.
OUTPUT PAGE : This page is only for showing the result of query to user. The retrieved data
shown at this page.
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5. DATASET
Several categories are formed using data fed in database using blood pressure measured, Body
Mass Index(BMI), body status and output disease.
DATA STRUCTURE TABLE
DATASET TABLE
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6. Methodology
The core objective of our project is to develop a web application using data mining concept
accompanied by HTML, CSS, JS (JavaScript) technology and MYSQL. The whole process can
be termed as “knowledge discovery process, (KDD)”. This is because here we need to predict the
disease for user input symptoms where the predicted disease is in the form of information or
knowledge. The raw data is collected from the world wide web and relevant data for our purpose
has been selected for further processing. Data is feed into the database using adminer tool and
various tuples have been formed for different categories.
Rule Based Methodology:
The methodology used in Health Care System is rule based. Here we define rules for finding
which user falls under which category defined manually. We have different sets for different kind
of users according to the input fed by them.
A rule-based expert system consists of a knowledge base and an inference engine. The
knowledge engineering process aims at designing and evaluating the knowledge base, and
implementing a proper inference engine. The process of building the knowledge base involves
the selection of a knowledge representation method, knowledge acquisition, and possibly low-
level knowledge encoding. In order to create an inference engine a reasoning technique must be
selected, and the engine has to be programmed. The first decision that has to be made is one
concerning knowledge representation method. It is widely recognized that there is no single
formalism suitable to represent knowledge for all purposes.
A variety of formalisms and structures is needed to represent knowledge. In the field of
rule-based expert systems the knowledge representation method is a systematic way of
“encoding” what an expert knows about some domain. However “encoding” means here rather
“describing” then “encrypting”. While there are numerous knowledge representing methods, the
logic-based ones are essential to the theory and practice of rule-based systems and expert
systems in general. Although propositional calculus is a simple logical system, it can serve as a
practically useful language for encoding rule-based systems. Further, both analysis and design of
such systems is relatively simple.
The most basic logical form of propositional rules is as follows : p1 ∧p2 ∧. . .∧pn −→ h.
This form of a rule is logically equivalent to a Horn clause, provided that all the literals are
positive. A more complex rule may contain conclusion part composed of several propositions. In
forward-chaining systems rules are applied by checking if their preconditions are satisfied.
Whenever a rule is fired, its conclusion is added to the current knowledge base. Propositional
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7. rule-based systems can take various visual forms incorporating some structural representation;
most important are: decision tables and decision trees. Decision tables are an engineering way of
representing production rules. Conditions are formed into a table which also holds appropriate
actions. Classical decision tables use binary logic extended with “not important” mark to express
states of conditions and actions to be performed. The main advantage of decision tables is their
simple, intuitive interpretation. One of the main disadvantages is that classical tables are limited
to binary logic. Decision trees are an important representation, since the tree-like representation
is readable, easy to use and understand. The root of the tree is an entry node, under any node
there are some branching links.
Prediction:
The prediction as it name implied is one of the data mining techniques that discovers relationship
between independent variables and relationship between dependent and independent variables.
For instance, prediction analysis technique can be used in blood donors to predict the behaviour
for the future if we consider donor is an independent variable, blood could be a dependent
variable. Then based on the historical data, we can draw a fitted regression curve that is used for
donor’s behaviour prediction. Regression technique can be adapted for predication. Regression
analysis can be used to model the relationship between one or more independent variables and
dependent variables. In data mining independent variables are attributes already known and
response variables are what we want to predict. Unfortunately, many real-world problems are not
simply prediction.
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8. Working of the System
We have certain sets of input taken from the user like age, gender, blood pressure, height, weight
and if the person is doing exercise on daily basis or not. On considering these inputs and based
on the data sets stored in database we can predict the disease or illness causing and how can a
person overcome the disease.
Using these inputs we also provide few comments related to the disease and kind of
exercises user can put upto. Doctors’ address list is also provided to the user so that they can
consult any doctor easily.
The system uses tuples defined in database and the rule based methodology to determine
the disease on basis of input provided.
Disease Prediction : - Patient will specify the symptoms caused due to his illness. The system
will ask certain symptoms regarding his illness and then predict the disease based on the
symptoms specified by patient.
Result : - The result will include user’s BMI, disease, what you should do, exercise and a diet
chart, blogroll. All the tuples have different diseases and related to that we have different sets of
exercises and diet chart.
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Body Status of the user
Probable Diseases
Set of Inputs help in determining
Preferred Exercises for the user
Body Mass Index (B.M.I)

9. CODE
BMI Formula Code
INPUT PAGE
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10. Logical Page Code
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11. EXPERIMENTAL RESULT
INPUT PAGE
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12. OUTPUT PAGE
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13. PERFORMANCE ANALYSIS
In the designing of “Smart Card based Online Health Care System”, the following issues have
been considered:
1. If a patient is registered in one hospital and integration and retrieval of patient information is
not possible in the hospital system then while taking the consultation with doctor, patient may
easily forget to inform about his/her allergy to the medicine and thus, may not be able to explain
the previous treatments properly which might result in incorrect prescriptions to the
patient.Keeping this in mind, a Smart Card based online Health Care System is being proposed.
The servers for the hospitals with suitable configuration are required. All the local servers
of the individual hospitals will be connected with the centralized main server of this system
through internet. A smart card reader / writer unit needs to be attached to each computer of the
hospital system. The proposed health care application will be installed to all the local servers of
the hospitals.
These hospitals will be connected via intranet and internet. The patient smart card stores some
important information like unique patient Id, name, sex, date of birth, blood group etc. As per the
registration number/ patient-ID, patient details like treatment prescriptions, test reports, images
like MRI, CT-scan etc. will be stored in the database of the hospital local server. On the basis of
stored details of the patient, doctor can prescribe the proper medicine. For fast retrieval of patient
data, different indexing techniques have been proposed in MySQL. For improving the
performance of our health care system, we have used various tuning techniques available in
MySQL.
2. An application, Smart Card based online Health Care System has been developed that can
serve large number of hospitals and provide an interface for their interaction. As per the
application, the system will generate unique registration number/ patient Id for every patient. To
increase the performance of the application, we have considered the following database and
application design aspects.
1. Normalization of tables.
2. Imposing necessary constraints on tables.
3. Selecting best possible structure for queries.
4. Managing insert operation to reduce load from server.
5. Front end should be strong enough to reduce basic user faults and provide easy
interaction
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14. CONCLUSION
Data mining can be beneficial in the field of medical domain .However privacy, security and
unable to log into the account are the big problems if they are not addressed and resolved
properly. It describes about the proposal of hybrid data mining model to extract classification
knowledge for aid of various diseases in clinical decision system and presents a framework of
the tool various tools used for analysis.
Sometimes the situation occurs when you need the doctor’s help immediately, but they
are not available due to some reason. In our project, we have designed a new health prediction
system, which is an online system, and various patients from any locations can view it.
This system have both advantages as well as disadvantages. The system is not fully
automated, we may need doctors’ help for recognising diseases. Our system comprises of main
components such as user’s age, height, weight, blood pressure, gender, doing exercise or not. On
these basis we have calculated disease that person can acquire , exercises recommendation, diet
chart. Thus, it allows the users to get analysis on the symptoms they give for predicting the
disease they are suffering from.
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15. REFERENCES
BOOK :
1. Adibi, Sasan, ed. (February 19, 2015). Mobile Health : A Technology Road Map
Springer. p.1.
2. "S.M.A.R.T. Attribute: Current Pending Sector Count", Knowlege Based, Acronis
3. Modern Information Retrieval( Ricardo Baeza-Yates, Berthier Ribeir-Neto )
4. Fundamentals of database systems( Ramez Elmsari,Shamkant B.Navathe )
5. Database System Concepts (Avi Silberschatz · Henry F.Korth · S. Sudarshan )
WEBSITE :
1. http://nevon.com/smart-health-prediction-using-data-mining/
2. https://www.w3schools.com/html
3. https://www.w3schools.com/php/default.asp
4. https://httpd.apache.org
5. https://www.mysql.com
6. https://www.coursera.org/courses?languages=en&query=database&userQuery=database
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