Prediction and Classification of Cardio Vascular Diseases using Ensemble Learning
Chaitanya Singla, Bidush Kumar Sahoo, Ravneet Kaur, Pramananda Sahu, Harpal Singh, Upinder Kaur
Abstract
Heart is a key component of living organisms. The heart disease can be the most fatal condition in the world, in which the heart fails to transport the proper quantity of blood to other areas within the human body. Prediction and diagnosis of heart-related diseases require greater precision, accuracy because even a small mistake could lead to fatigue or even death. There are a lot of death cases involving the heart, and the numbers are increasing daily. The method of diagnosing heart disease using the traditional method is not accepted as reliable in many ways. To tackle the issue, it is imperative to have a predictive system to raise awareness of illnesses. Machine learning is the field of Artificial Intelligence(AI) and offers a prestigious service to predict all kinds of events that takes training from natural phenomena. In this work various machine learning algorithms such as decision tree, K-Nearest Neighbor, AdaBoost are used. The main goal of this research is to predict the disease. After implementing all the algorithms, evaluation of these algorithms is done based on accuracy. The dataset is freely downloadable from the Kaggle website.