Heart Disease Prediction using Machine Learning and Deep Learning Algorithms
Kuldeep Vayadande, Rohan Golawar, S. Khairnar, Arnav Dhiwar, Sarthak Wakchoure, Sumit Bhoite, Darpan Khadke
Abstract
WHO reports states which are nearly 1 crore 20 lakhs deaths happen due to heart diseases. In past years heart disease or cardiovascular disease cause large impact in medical industries, so they are really very dangerous and have a large impact worldwide. Although precious prediction of heart diseases or CD and also the 24-hour monitoring on patient is not possible because it requires lots of knowledge and time. Heart disease treatment or diagnosis are very complicated, mainly in the poor countries or the countries which not fully developed. Also because of not having proper medical attention or timely cure of disease it can lead to death of the person. The medical industry has a large amount of data and is continuously used by researchers to develop new science and technology to minimize the number of deaths happens due to heart disease. Lots of data mining and ML techniques or algorithms are available to fetch the data from databases and use this fetched data to predict the heart diseases very accurately. In this heart disease model, we used machine learning algorithms and deep learning algorithms, we have implemented all algorithms on the dataset. The dataset used is from Kaggle which is of 303 rows and 14 attribute. The algorithm that are used in the model are Logistic Regression, NB, K-NN, SVM, Multi-Layer Perceptron’s, Artificial Neural Network, Decision Tree, Random Forest, XG Boost and Cat Boost.