A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage

Document Type : Original Article


Department of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran,Iran


Divorce will have destructive spiritual and material effects, and unfortunately, in this regard recent statistics have shown that solutions provided for its prevention and reduction have not been effective. One of the effective solutions to reduce divorce in society is to review the background of the couple, which can provide valuable experiences to experts, and used by experts and family counselors. In this article, a method has been proposed that uses data mining and deep learning to help family counselors to predict the outcome of marriage as a practical tool. Reviewing the background of thousands of couples will provide a model for the coupe behavior analysis. The primary data of this study was collected from the information of 35,000 couples registered in the National Organization for Civil Registration of Iran during 2018-2019. In the current work, we proposed a method to predict divorce by combining a convolutional neural network (CNN) and long short-term memory (LSTM). In this hybrid method, key features in a dataset are selected using CNN layers, and then predicted using LSTM layers with an accuracy of 99.67 percent. A comparison of the method used in this article and Multilayer Perceptron (MLP) and CNN suggests that it has a higher degree of accuracy.


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Touba Torabipour has a bachelor's degree, master's degree in computer engineering, majoring in software engineering. Her areas of interest include Deep learning, reinforcement learning, machine vision, artificial intelligence, and the Internet of Things. She has published more than 6 articles in prestigious Iranian and international journals and conferences.


 Seyedeh Safieh Siadat, Assistant Professor, Payame Noor University, has a bachelor's, master's and doctoral degree in computer engineering majoring in software engineering. She has been a full-time faculty member at Payame Noor University since 1996. His areas of interest include the Internet of Things, game theory, machine learning, and cloud computing. She has published more than 42 articles in prestigious international journals and conferences.


Hossein Taghavi completed his bachelor's degree in electrical engineering from Shiraz Industrial University in 2014, he received a master's degree in Information Technology Management in 2022 at Tehran Gharb PayameNoor University. His field of activity is the development of computer networks.