• Erum Parkar
• Hamira Shaikh
• Taskeen Merchant
• Sonali Suryawanshi

Prediction of critical diseases and Machine Learning go hand in hand as the predictive models use Machine Learning algorithms. But accurate prediction on the basis of symptoms becomes too difficult for doctors. The most challenging task is the accurate and correct prediction of the disease. There are instances where online medical help or healthcare advice is easier or faster to grasp than real world help. People often feel reluctant to go to hospital or physician for minor symptoms. However, in many cases, these minor symptoms may trigger major health hazards. This project is based on creation of a web application for detecting fatal diseases such as Heart disease, Diabetes using Machine Learning The project implements classification models such as K Nearest Neighbor (KNN) and Random Forest Classifier. The values and symptoms on various parameters entered by the user will enable the model to predict if a person is at a risk of disease. The system will provide the results based on the features extracted. It also saves a lot of time for the patients or doctors and they can further proceed for treatments or other procedures faster.

Machine Learning, K Nearest Neighbor (KNN) Classifier, Random Forest Classifier.

"HEALTH RISK ESTIMATOR USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 4, page no.115-118, April-2021, Available :http://www.jetir.org/papers/JETIR2104217.pdf

"HEALTH RISK ESTIMATOR USING MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 4, page no. pp115-118, April-2021, Available at : http://www.jetir.org/papers/JETIR2104217.pdf