Prediction of Heart Disease using Supervised Machine Learning Algorithms and Streamlit

Author Name : Kshitija Kiran Manore, Rutuja Rajendra Lamkane, Prof. Sanjay Kadam

ABSTRACT

Heart disease is one of the most critical human diseases in the world that affects human life very badly around the world. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. And, so heart disease is one of the major reasons for the increase in the death rate. Therefore, an accurate and timely diagnosis is very important to prevent heart failure and to treat heart disease patients at an early stage. To deal with this problem it is essential to develop a heart disease prediction system. Hence, in this paper, we have put forth the machine learning-based prediction system which classifies patients into two categories with heart disease and without heart disease. Three algorithms Support Vector Machine (SVM), K- Nearest Neighbor (KNN), and Random Forest are used in the prediction system and we have used the dataset from UCI Repository.

Keywords: Machine Learning, Heart Disease, Data Mining, Streamlit, KNN, SVM, Random Forest