Comparative Study of Decision Tree, AdaBoost, Random Forest, Naïve Bayes, KNN, and Perceptron for Heart Disease Prediction

Globally, cardiovascular diseases (CVD) are estimated to account for more than 32% of all deaths. Consequently, CVD has become a global health problem, and timely diagnosis is essential (WHO, 2021). Screening for risk factors accelerates the diagnosis and management of CVD, resulting in a more effec...

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Main Authors: Maydanchi, Mohammad, Ziaei, Armin, Basiri, Mina, Azad, Alireza Norouzi, Pouya, Shaheen, Ziaei, Mehrbod, Haji, Fatemeh, Sargolzaei, Saman
Format: Conference Proceeding
Language:English
Subjects:
AdaBoost
Cardiovascular diseases
Classification models
Data models
Decision Tree
Decision trees
Forestry
Heart
KNN
Medical diagnosis
Naïve Bayes
Neural networks
Perceptron
Predictive models
Random Forest
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Summary:Globally, cardiovascular diseases (CVD) are estimated to account for more than 32% of all deaths. Consequently, CVD has become a global health problem, and timely diagnosis is essential (WHO, 2021). Screening for risk factors accelerates the diagnosis and management of CVD, resulting in a more effective and rapid response, reducing the risk of death. This article compares six classification models, AdaBoost, Random Forest, Decision Tree, KNN, Naive Bayes, and Perceptron, to predict CVD symptoms. Based on CDC data collected from Kaggle, classification models were compared with the approach of examining effective factors to predict heart disease. Since the data set was imbalanced, the study performance was measured by AUC and F 1 -score in Class 1, which is the critical class in this dataset. AdaBoost is found to have the highest AUC and F 1 -score, respectively, of 0.828 and 0.37, while Decision Tree has the lowest AUC of 0.595 and F 1 -score of 0.25.
ISSN:1558-058X
DOI:10.1109/SoutheastCon51012.2023.10115189