Heart Disease Detector Web App

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 Yes, you heard that right. We are back with more Machine Learning content and another web app (Yup, machine learning is definitely very cool). This app is similar to our first web app i.e. Diabetes Detector. This is another disease detection web app which detects if you have any cardiovascular diseases (CVD) based on certain features such as age, resting blood pressure, cholestrol, maximum heart rate achieved, ST depression induced by exercise and number of major vessels coloured by flouroscopy. ST depression refers to a finding on an electrocardiogram, wherein the trace in the ST segment is abnormally low below the baseline. Fluoroscopy is an imaging technique that uses X-rays to obtain real-time moving images of the interior of an object. This app is another example of the use of machine learning and data science in the field of medicine which I find very innovative. 

This web app is based on machine learning and uses the Random Forest Classifier algorithm. The web app uses interactive visual and graphical interpretations to display the outcome and compare the input parameters given by the user. The sidebar sliders help in changing the values of the parametres for determination of the result. The graphs compare the values of the patient with others ( both with patients having heart diseases and not having heart diseases). It also provides the accuracy of the result. This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have a heart disease or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases. 

We have deployed the app using Streamlit. It is an open source framework that allows data science teams to deploy web apps fairly easily. It's one of the best hosting services I've used and it's great for quick and easy deployment of web apps. The app is coded in python. 

This web app helped build upon my experience and comfort over machine learning and its vast applications. It is one of our many projects in the field of computer science and specifically data science. We hope to deploy more articles in the future and share them with you. Please feel free to add on and contribute to the project and don't hesitate to drop any suggestions. The link for the web app is: https://share.streamlit.io/pranav-coder2005/heartdisease/main/app.py  


Disclaimer: This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have any cardiovascular disease or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases.

The awareness month for heart health in the United States is February. National wear red day is observed on the first Friday of February of each year to increase awareness about heart diseases. World Heart Federation, World Health Organization and many other such organizations run campaigns dedicated to the prevention, diagnosis and control of cardiovascular diseases.The World Heart Federation is a nongovernmental organization based in Geneva, Switzerland, formed in 1972 In 1978 the International Society of Cardiology merged with the International Cardiology Federation to form the International Society and Federation of Cardiology. The federation hosts the World Congress of Cardiology. World Heart Day" was founded in 2000 to inform people around the globe that heart disease and stroke are the world’s leading causes of death. World Heart Day is celebrated every year on 29 September. On that note, let's increase cardiovascular health awareness and make the world much healthier. 

Comments

  1. ArnavJune 26, 2021 at 4:06 PM

    Very nice, have you tried other classification problems in machine learning ?

    ReplyDelete
    Replies
    1. Pranav SawantJune 27, 2021 at 11:43 PM

      Yeah I have tried multiple classification problems based upon open source datasets, I'll keep uploading as and when they're ready for deployment. I'll also look to work upon some self made datasets for deep learning and computer vision in the future

      Delete
    2. ArnavJuly 3, 2021 at 12:53 PM

      Great, look forward to it !

      Delete

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