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20 April 2023 Hyperparameter tuning of GDBT models for prediction of heart disease
Qingcong Lv
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Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126022J (2023) https://doi.org/10.1117/12.2668449
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China
Abstract
According to World Health Organization, heart disease has remained the leading cause of death for the last twenty years.Patients with heart disease will have palpitations, chest pain, dyspnea and other characteristics. How to diagnose heart disease based on these characteristics is an important research topic for doctors and scientists. Classifying heart disease with machine learning is known as a reliable and accurate heart disease detection technique. In this study, we aim to find a good model for predicting heart disease. To find the best model, we compare three GBDT(Gradient Boosting Decision Tree) models i.e., XGBoost, LightGBM and CatBoost to identifywhether a person has heart disease or not. We compare the three models tuning the hyperparameters to improve heart disease predicting. To compare results, we create three base models with default hyperparameters. The findings demonstrate that the CatBoost with hyperparamer tuning model as the the most appropriate heart disease prediction model with accuarcy of 90%. Results indicated that tuning the hyperparameter improved modeling results.
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Qingcong Lv "Hyperparameter tuning of GDBT models for prediction of heart disease", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126022J (20 April 2023); https://doi.org/10.1117/12.2668449
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KEYWORDS
Heart
Machine learning
Decision trees
Performance modeling
Random forests
Data modeling
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