Heart Disease Prediction System

Project Title

Heart Disease Prediction System

Project Area of Specialization Artificial IntelligenceProject Summary

Heart disease, also known as cardiovascular disease (CVD), includes several conditions that influence the heart. Heart disease was the major cause of casualties in different countries including Pakistan. There are many factors that can cause heart diseases like pulse rate, cholesterol, blood pressure, age, diabetes and many more. An estimated assumption is that in the whole world there is more than 17.5 million deaths occur because of cardiovascular disease. The threat level these diseases possess can be checked by various machine learning models like Decision Tree, Naïve Bayes, K Nearest Neighbor, Support Vector Machines. For the prediction of these diseases and the threat level it possesses, Machine Learning aspects that can play a major role in it. The main challenge in today's healthcare is provision of best quality services and accurate diagnosis. In recent years heart diseases are found as the major source of death in the world, but they are also the ones that can be managed and controlled effectively. The whole accuracy and effectiveness for the management of a disease lies on the proper time of detection of that disease.

Data mining is used to extract hidden patterns and relationships from large databases by combining statistical analysis, machine learning and database technology. Data mining is the analysis of large data sets to discover patterns and use those patterns to  predict the likelihood of future events and to build predictive models. Data Mining and Machine Learning methods like Decision Tree have been used to predict these diseases by calculating the accuracy on a dataset. The term “cardiovascular disease” includes a wide range of conditions that affect the blood vessels and the heart, and the way blood is circulated and pumped through the body. According to the World Health Statistics 2012, 3 adults worldwide has raised blood pressure (it is a condition that causes around half of all deaths).In the United States, heart diseases kill one person in every 34 seconds .Diagnosis of a heart disease is an important and complicated task that needs to be executed accurately and efficiently. It is often made by doctor’s knowledge & experience. Therefore, it is beneficial to make an automatic medical diagnosis system. Hence, heart disease prediction system will help us to diagonose the the root cuases on heart disease. It will not only tell us what is causing heart disease it will also tell us how has been caused. We can help medical commnunity by providing such faster system. System is based on web and mobile app. Moreover, we are also implementing call agent so we can get the answer through call. 

Project Objectives

Our objectives include helping medical community to reduce errors in heart disease diagnosis and curing process. Our targeted audience can use this applicaton to get detail reviews about their heart. 

Integration of clinical decision support with computer-based patient records could reduce

• Medical errors
• Enhance patient safety
• Decrease unwanted practice variation
• Reduce test time
• Improve patient outcome.
• Study and Implemenation of different algorithm

This suggestion is promising as data modeling and analysis tools, e.g., machine learning have the potential to generate a knowledge which can help to significantly improve the quality of clinical decisions as well as variable wise impact.

Project Implementation Method

We are using machine learning algorithms to generate computer understanble knowledge using the tests which are already done. Algorithm generates models and models knows about the heart disease status and we can extract what caused heart disease. All of this happens with the tests report we provide to computer and computer generates the knowledge. The next phase is about the developing the web and mobile applications. 

Full stack architecture is the most modern, a latest architecture of highly enterprise web and mobile applications. In conventional architectures, we have a single server where the views are served, databases and business logic are implemented on the single place but in modern approach which is full stack development, we have the backend as a separate service and frontend as a separate application. Similarly, database is stored somewhere else. These three layers are consecutively called the business logic, presentation layer and datalink layer. In our application we do not have such use of data link layer instead we have the model. Model is stored in a file and we read the predictions from that file in the business logic.

Benefits of the Project

System will help medical community to detect heart disease. Doctors can have the reviews of machine. Machine makes less errors. We can use artificial intelligence of computer to cure the heart disease of patients.

Integration of clinical decision support with computer-based patient records could reduce

• Medical errors
• Enhance patient safety
• Decrease unwanted practice variation
• Reduce test time
• Improve patient outcome.

Technical Details of Final Deliverable

Final deliverable will be a mobile, web app and a chatbot which will deploy on call agent. 

Mobile and Web apps are the client applications which user will directly interact and take benefits from it. 

While chatbot will be deployed on call agent. User will have to call upon a phone number, give their input parameters and results. 

These clients applications will use Google App Engine backend services to serve the report of patients.

Final Deliverable of the Project Software SystemCore Industry MedicalOther Industries IT , Medical Core Technology Artificial Intelligence(AI)Other Technologies Cloud InfrastructureSustainable Development Goals Life on LandRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	60000
Google Cloud Fees	Equipment	1	30000	30000
Call Agent Phone Cost	Equipment	1	20000	20000
Libraries, Integration etc	Miscellaneous	1	10000	10000