Abstract
Nearly 17.5 million deaths from cardiovascular disease occur worldwide. Currently, India has more than 30 million heart patients. People’s unconscious attitudes towards health are likely to lead to a variety of illnesses and can be life threatening. In the healthcare industry, large amounts of data are frequently generated. However, it is often not used effectively. The data indicates that the generated image, sound, text, or file has some hidden patterns and their relationships. Tools used to extract knowledge from these databases for clinical diagnosis of disease or other purposes are less common. Of course, if you can create a mechanism or system that can communicate your mind to people and alert you based on your medical history, it will help. Current experimental studies use machine learning (ML) algorithms to predict risk factors for a person’s heart disease, depending on several characteristics of the medical history. Use input features such as gender, cholesterol, blood pressure, TTH, and stress to predict the patient’s risk of heart disease. Data mining (DM) techniques such as Naive Bayes, decision trees, support vector machines, and logistic regression are analyzed in the heart disease database. The accuracy of various algorithms is measured and the algorithms were compared. The result of this experimental analysis is a 0 or 1 result that poses no danger or danger to the individual. Django is used to run a website.
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Appendix
Appendix
Implementation—Web Application
Included here is the implementation of web application through feature driven development. Each activity of feature driven development is discussed with artifacts produced during that activity.
The web application has been developed using Django framework of python. It utilizes the MVT (Model View Template) architecture.
Development of Overall Model
During this activity of feature driven development, software requirement specification document was prepared for capturing the requirements. ER Diagram and requirement specification document was designed. After that, for the completion of this activity, a domain object model was prepared along with the overall application architecture.
Functional Specifications
Included in this section are the functional/non-functional requirements of the systems along with the use-cases and wireframes.
Functional Requirements
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The system allows users to predict heart disease.
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The system allows users to create an account and login.
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The system allows the users to update their profile and password.
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The system provides login for admin.
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The system should allow administrator to monitor and remove inappropriate datasets and code.
Non-functional Requirements
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The website should be responsive and have consistent across different screen sizes and resolutions.
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The website should provide user information about different values used during the prediction.
Architecture
The major components of Django project architecture are models, views and templates along with urls.py, settings.py, admin.py and forms.py.
Our Simulation architecture has been described as follows:
Figure 20 shows the architecture of our project and how different files are distributed in different directories.
Models
A model is a definitive data source that includes the underlying context and behavior of the data. A model is usually a table in a database. Each part of the table in the database is a model property. Django provides a set of automatic programming interfaces (APIs) for database applications for the convenience of the user.
View
The file view is short. This file contains a Python function that receives a web request and returns a web response. The answer is an XML document or HTML content or a “404 error”. The function logic of the function is not optional until it returns the desired response. To link a view function to a specific URL, you must use a structure called URLconf that maps the URL to a display function.
Template
Django templates are simple text files that can generate text-based templates such as HTML and XML. The template contains tags and variables. When evaluating the pattern, the variable is replaced with the result. Pattern logic is controlled by tags. You can also change variables using filters. For example, a small filter can convert a variable from uppercase to lowercase.
Figures 21 and 22 show how website and Analysis works. It shows how a request is sent and response is sent back.
The working of a template is as follows. A request for a resource is made by any user from the template. Than Django works as a controller and check the availability of the resource in the URL. If the URL matches with any view than Django returns it to the template as a response.
Figure 23 shows the Cleveland data set used and machine learning algorithm applied by python software.
Documentation (User Manual):
For Users
Steps that how the system is executed:
Firstly the user has to connect his phone to internet by the medium of mobile data pack or by the medium of Wi-Fi.
Then the user has to download or clone the project. Steps to run
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create virtual env ex. mkvirtualenv mytest_env (optional)
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pip install -r requirements.txt
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python manage.py migrate
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python manage.py runserver.
Steps to execute Registration and Login
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Then the user must register into the application using his basic personal details like username and email.
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If the user is already registered, then the user can log into the application using his login credentials including username and password.
Prediction of Heart Risk
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Now to predict the risk of heart disease, user can enter the values of various parameters on the basis of which his risk factor will be calculated.
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After entering all the values, click on Predict button.
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The page will be reloaded and the result will be shown according to 4 ML.
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If result is 1, user has risk of heart disease. If result is 0, user does not have a risk of heart disease.
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If two or more models give result as 1, the user has a risk of heart disease.
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The user can also view his profile and previous predicted results by clicking on Profile tab.
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Rastogi, R., Chaturvedi, D.K., Satya, S., Arora, N. (2020). Intelligent Heart Disease Prediction on Physical and Mental Parameters: A ML Based IoT and Big Data Application and Analysis. In: Jain, V., Chatterjee, J. (eds) Machine Learning with Health Care Perspective. Learning and Analytics in Intelligent Systems, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-40850-3_10
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