CARDIOease💖

VIT Hack PROJECT

Introduction 🎈:

We still fail to provide good medical facilities in rural or remote areas of our country. We lack medical infrastructure to ensure proper treatment of each and every individual of the country. Generally we see that medical officers who have a strong work experience are posted in busy city hospitals while the rural areas are neglected, and often cost a fortune, which is not affordable for most of the patients.

Objective 🎯:

The main objective of this project is to ensure proper diagnosis/identification of heart related diseases by just getting simple inputs from the medical report generated after conducting the tests. Thus, if a doctor posted in some rural area lacks work experience, he can get the help of this application to predict or at least think in the right direction.

Implementation ✔:

We plan to design an android application which will input certain parameters from the test reports of the patient, and give predictions, whether or not the patient suffers from any heart disease.

Application 👩‍💻:

The main application of this project will be in rural health sectors where medical staff and officials can enter data from the report into the android application and let the application do the prediction

The main workflow 💡:

Data of the patient is collected from the previous records and reports of the patient. This data is fed into the application . If the user is unaware of what to feed in a certain field, UI is well designed to guide the user in such a situation. After the complete data is fed to the application the task is handed over to the Machine Learning Algorithms to predict the problem as per the input with the minimum possibility of error. Once the prediction is made, the application displays the predicted results on a separate screen of the application.

How do we plan on building upon our idea ?

• The first step would be to build the UI.
• To create the machine learning model, we will identify the suitable attributes.
• Create a machine learning pipeline. And train the model.
• Create the model, dump it to object file.
• Once, the model is created, we put an EditText view for taking inputs from users.
• Then send data to the Machine Learning model.
• Displaying the output.

Workflow 💹

Tech Stack 💻:

1. Android Studio
2. Python
3. Figma

Final Results ✨:

The final result will be a fully deployed android app which predicts the heart related issues from the input provided. This application basically helps the Medical staff to diagnose the disease based on their knowledge and previous statistics(for which the ML model is used).

Future Aspects 🤖:

We plan to expand our area of interests, and cover more healthcare sectors to benefit the unempowered people. The research for the best model accuracy will continue. We will take into consideration more attributes because that, will improve the model accuracy.

Link for the whole Android App Project 📱:

https://github.com/shruti8301/CARDIOease_final