Data Science and Intelligent Computing Techniques
Classification Comparison of Different Boosting Algorithms to Predict and Classify Conditions of Heart Disease
Authors: Vijay Mane, Pranav Belgaonkar, Harshad Dabhade and Ameya Gandhe
Publishing Date: 20-12-2023
ISBN: 978-81-955020-2-8
Abstract
In the modern era, heart diseases account for the majority of the casualties. Timely and more efficient identification of every particular heart disease proves to be a vital factor in the healthcare sector. This paper presents a system which can predict and classify conditions of heart disease using several ML techniques called boosting algorithms. Cleveland Dataset is used to train this model. This particular dataset consists of 14 variables measured on 1025 individuals who have different health conditions and some having heart disease. The models used include boosting algorithms such as AdaBoost, XGboost, Gradient Boosting, CatBoost and LightGBM. We have compared all these boosting algorithms and calculated their respective accuracy and confusion matrices.
Keywords
Heart Disease, Machine Learning, Gradient Boosting, XGBoost, ADABOOST, CATBOOST, Light GBM.
Cite as
Vijay Mane, Pranav Belgaonkar, Harshad Dabhade and Ameya Gandhe, "Classification Comparison of Different Boosting Algorithms to Predict and Classify Conditions of Heart Disease", In: Satyasai Jagannath Nanda and Rajendra Prasad Yadav (eds), Data Science and Intelligent Computing Techniques, SCRS, India, 2023, pp. 869-876. https://doi.org/10.56155/978-81-955020-2-8-74