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

Maydanchi, Mohammad; Ziaei, Armin; Basiri, Mina; Azad, Alireza Norouzi; Pouya, Shaheen; Ziaei, Mehrbod; Haji, Fatemeh; Sargolzaei, Saman

doi:10.1109/southeastcon51012.2023.10115189

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…In the realm of economic forecasting, Tehranian, K. [25] employed various machine learning techniques such as Probit, Logit, Elastic Net, RF, Gradient Boosting, and Neural Networks to predict economic recessions in the United States. Concerning health issues and disease diagnosis, M. Maydanchi et al [26] compared six classification models, including AdaBoost, RF, Decision Trees, K-Nearest Neighbors (KNN), Naïve Bayes, and Perceptron, to predict CVD symptoms. In the field of engineering, Ghasemi, A., Naser M.Z.…”

Section: Introductionmentioning

confidence: 99%

Empirical Study of ESG Score Prediction through Machine Learning—A Case of Non-Financial Companies in Taiwan

Lin,

Hsu

2023

Sustainability

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In recent years, ESG (Environmental, Social, and Governance) has become a critical indicator for evaluating sustainable companies. However, the actual logic used for ESG score calculation remains exclusive to rating agencies. Therefore, with the advancement of AI, using machine learning to establish a reliable ESG score prediction model is a topic worth exploring. This study aims to build ESG score prediction models for the non-financial industry in Taiwan using random forest (RF), Extreme Learning Machines (ELM), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) and investigates whether the COVID-19 pandemic has affected the accuracy of these models. The dependent variable is the Taiwan ESG Sustainable Development Index, while the independent variables are 27 financial metrics and corporate governance indicators with three parts: pre-pandemic, pandemic, and the entire period (2018–2021). RMSE, MAE, MAPE, and r2 are conducted to evaluate these models. The results demonstrate the four supervised models perform well during all three periods. ELM, XGBoost, and SVM exhibit excellent performance, while RF demonstrates good accuracy but relatively lower than the others. XGBoost’s r2 shows inconsistency with RMSE, MAPE, and MAE. This study concludes the predictive performance of RF and XGBoost is inferior to that of other models.

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Section: Introductionmentioning

confidence: 99%