Prediction of Diabetes, Cardiac and Parkinson’s disease using machine learning and streamlit framework

The development of new scientific instruments and the emergence of new technologies have greatly streamlined, expedited, and eased human life. However, just like a coin has two sides, the advent of development has drawbacks of its own. Specifically leading to a more inactive and sedentary as well as numerous lifestyle diseases like diabetes and elevated stress levels, all of which contribute to a host of cardiac diseases. Extended use of social media devices damages the retina and affects neurons, which can result in Parkinson's disease. The aim of this research is to forecast the probability of lifestyle diseases by utilizing diverse Neural Networks and Machine Learning Algorithms. The algorithms that are employed are Random Forest Classifier, XGBoost, and SVM. Moreover, this prediction system has an interactive user interface that can be deployed and hosted either locally or on world wide web and can be accessed by all.