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

Document Type : Original Article

Authors

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

Abstract

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.

Keywords

References

  • Gorunescu, Data Mining: Concepts, models and techniques, vol. 12, Springer Science & Business Media, 2011, p. 201.
  • Brownlee, Deep learning with Python: develop deep learning models on Theano and TensorFlow using Keras. Machine Learning Mastery, 2016.
  • K. Yöntem, A. Kemal, T. Ilhan, and S. Kiliçarslan, “Divorce prediction using correlation based feature selection and artificial neural networks,” Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, vol. 9, no. 1, pp. 259-273, 2019.
  • Nasser, “Predicting whether a couple is going to get divorced or not using artificial neural networks,” International Journal of Engineering and Information Systems (IJEAIS), vol. 3, no. 10, pp. 49-55, 2019.
  • Ranjitha and A. Prabhu, “Improved divorce prediction using machine learning- particle swarm optimization (PSO),” 2020 International Conference for Emerging Technology (INCET), Belgaum, India, IEEE, June 2020, pp. 1–5,
  • Kong and T. Chai, "Is your marriage reliable? divorce analysis with machine learning algorithms," in Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence, 2020, pp. 1-4.
  • Hafidz, Sfenrianto, Y. Sfenrianto, Y. Pribadi, E. Fitri, and Ratino, “ANN and SVM algorithm in divorce predictor,” International Journal of Engineering and Advanced Technology, vol. 9, no. 3, pp. 2523–2527, 2020.
  • Sharma, A. S. Chudhey, and M. Singh, “Divorce case prediction using machine learning algorithms,” 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India, March 2021, pp. 214–219,.
  • Enggari and S. Defit, “Divorce Fact Detection Based on Internet User Behavior Using Hybrid Systems with Combination of Apriori Algorithm and K-Means Method,” Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika, vol. 8, no. 1, 2022.
  • Flores, S. Silva, S. Silva, A. I. Group, “Machine learning model to predict the divorce of a married couple,” 3C Tecnolog´ıa_Glosas de innovaci´on aplicadas a la pyme, pp. 83–95, 2021.
  • M. Sadiq Fareed, A. Raza, N. Zhao, A. Tariq, F. Younas, G. Ahmed, “Predicting divorce prospect using ensemble learning: Support vector machine, linear model, and neural network, ” Computational Intelligence and Neuroscience, vol. 2022, no. 1, p. 1-15, 2022.
  • Narendran, R. Abilash, and B. Charulatha, “Exploration of classification algorithms for divorce prediction,” in Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, Springer, 2021,pp. 291-303.
  • Fouladi, M. Ebadi, A. A. Safaei, M. Y. Bajuri, and A. Ahmadian, “Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio,” Computer communications, vol. 176, pp. 234-248, 2021.

 

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.

 

 

 

 

 

International Journal of Web Research
Volume 5, Issue 1
Winter-Spring
June 2022
Pages 88-95

Files

Share

How to cite

Statistics