sci-flag Decision Making and Artificial Intelligence Trends 3060-6500 1 1 2024 03 01 Predicting U.S. economic recessions and prosperity: a comparative study of machine learning models 34 51 199485 10.22034/dmait.2024.199485 EN Hamidreza Ghadiri Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran Mahmoud Gharehgozlou Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran 0009-0006-6211-7708 Journal Article 2024 05 15 The economic cycles, whether it is experiencing recessions or prosperity, serve as the foundation for numerous political and economic decisions. As a result, predicting economic cycles both domestically and globally presents a significant challenge for investors and economic stakeholders. In this study, we investigate the effectiveness of various machine learning (ML) models in predicting economic cycles. Firstly, employs two feature selection methods, including mutual information (MI) and analysis of variance (ANOVA), to select important features. Subsequently, classification models such as Gaussian naïve Bayes, logistic regression, support vector machine (SVM), decision tree, multi-layer perceptron (MLP) neural network, Random Forest (RF), AdaBoost, and voting are utilized to predict economic cycles across various timeframes, ranging from one season to four seasons. This study uses data from the United States (U.S.) economy to evaluate the performance of these models. The results demonstrate the superiority of the ANOVA method in feature selection and the high accuracy of Gaussian naïve Bayes, SVM, and voting models in predicting economic cycles, reaching up to 93% accuracy. Economic cycle Forecasting Machine learning models Feature selection https://dmait.sci-flag.com/article_199485_502975f6ce77f3c1f8d5880b4a823d65.pdf