Care with ML; Heart Disease Prediction
The Care with ML is a machine learning-based trained model that will be helpful for the medical sector to predict heart disease and provide such sort of valuable results and will assist the cardiologists to develop their opinions about heart diseases patients. These days? Cardiovascular diseases are
Project Title
Care with ML; Heart Disease Prediction
Project Area of Specialization
Project Summary
The Care with ML is a machine learning-based trained model that will be helpful for the medical sector to predict heart disease and provide such sort of valuable results and will assist the cardiologists to develop their opinions about heart diseases patients. These days’ Cardiovascular diseases are the leading cause of death globally and according to the WHO over 17.9 million people died every year due to cardiovascular heart diseases in the world, and around 80% of that get targeted by a heart attack. So, to overcome such a situation and reduce its increment we have decided to train a model on heart diseases, which will use the algorithms Logistic Regression, SVM, and Random Forest to provide predictions before any heart conditions.
Project Objectives
- to effectively predict if the patient suffers from heart disease
- to assists medical professionals/clinicians in preventing the rapid progression of heart disease at an early stage.
- to give real-time heart disease predictions.
- to provide accurate results based on historical data of the patient.
- It helps the doctors to take immediate decisions based on prediction.
- From medical history diseases can be in advance by assessing the risk factors of the patient.
Project Implementation Method
Users can create an account to use this web-based ML-enabled system. Users must log in or create an account using their name, email address, and password; after logging in, users can access the account's dashboard. Users may access the predict option through the dashboard; with this option, users can upload the patient's medical background file to forecast heart disorders; after the training and testing data, users can quickly determine if the patient has heart disease or not. If the user is a doctor, he or she can see who can see the requests from users via the dashboard. There is also an admin account that can validate doctors and users, as well as amend and delete their accounts. In our project we are following agile project management methodology named scrum methodology, through this methodology we done work in short cycles called sprints. we meet routinely to discuss current tasks and any potential hurdles in our project.
Benefits of the Project
- Easily predict heart diseases with an ML-enabled system
- Trained with ML algorithms
- It helps the heart suffered patients to diagnose disease on earlier stage.
- It helps the doctors to take the immediate decision based on prediction.
- It enables significant knowledge about patients’ history.
- It can predict and diagnose the heart diseases without need of heavy procedures and equipment
- Easy to use.
- Users friendly.
Technical Details of Final Deliverable
| This system will be based on machine learning algorithms and work as a prediction model to diagnose heart diseases, whether a patient has the disease or not. It can be run in any network environment. In this system, there is a user login panel. There are three types of users in this system. We have used Logistic Regression, Support Vector Machine and Random Forest, to get the highest accuracy further we also tuned parameters according to our model. |
Admin: Admin will have to manage all the administrative tasks like users' authentication, like verification of a user's status as a doctor, updating, deleting, and managing all the user accounts.
Doctor: Doctors will log in as doctors and see the requests from patients, also can negotiate according their diseases.
User: Users can access their dashboard and submit the necessary medical data to predict heart disease, as well as view the recommended doctor's list and institutes.
This system will be based on machine learning algorithms and work as a prediction model to diagnose heart diseases, whether a patient has the disease or not. It can be run in any network environment. In this system, there is a user login panel. There are three types of users in this system.
We have used Logistic Regression, Support Vector Machine and Random Forest, to get the highest accuracy further we also tuned parameters according to our model.
Final Deliverable of the Project
Core Industry
Other Industries
Core Technology
Other Technologies
Sustainable Development Goals
Required Resources
| Elapsed time in (days or weeks or month or quarter) since start of the project | Milestone | Deliverable |
|---|---|---|
| Month 1 | Presenting project idea and title selection | Done |
| Month 2 | Data collection | Done |
| Month 3 | Analyze and contextualize the data with according medical history | Done |
| Month 4 | Proposal approval and Submission | Done |
| Month 5 | Documentation (Introduction and Literature) | Done |
| Month 6 | Documentation (Literature & schema designing) | Done |
| Month 7 | Web designing | Done |
| Month 8 | Database designing | In process |
| Month 9 | Documentation | In process |
| Month 10 | Model creation, implementation and testing | In process |
| Month 11 | Final project report | In process |
| Month 12 | Final Submission | In process |
If you need this project, please contact me on contact@adikhanofficial.com
Intelligent and energy efficient irrigation monitoring system usin clo...
Internet Of Things (IoT)is a shared network of objects or things which can interact with e...
IoT Based Heart Attack Monitoring and Prediction System
IoT based heart attack monitoring and Prediction system is related to medical and healthca...
DESIGN AND CONTROL OF HIGH VOLTAGE DC-DC POWER CONVERTER SYSTEM
Dc-Dc converters with steep voltage ratio are usually required in many industrial applicat...
Gait Recognition System
Giat Recognition System is a system in which we identify people by the way they walk. it i...
Air jet manipulation of a droplet using superhydrophobic pathways
The aim of this project is to create a portable platform that is capable of automatically...