Efficient Prediction of Court Judgments Using an LSTM+CNN Neural Network Model with an Optimal Feature Set

Daniyal Alghazzawi, Omaimah Bamasag, Aiiad Albeshri, Iqra Sana, Hayat Ullah and Muhammad Zubair Asghar
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Daniyal Alghazzawi: Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia
Omaimah Bamasag: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia
Aiiad Albeshri: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80200, Saudi Arabia
Iqra Sana: Institute of Computing and Information Technology (ICIT), Gomal University, Dera Ismail Khan 29220, Pakistan
Hayat Ullah: Institute of Computing and Information Technology (ICIT), Gomal University, Dera Ismail Khan 29220, Pakistan
Muhammad Zubair Asghar: Institute of Computing and Information Technology (ICIT), Gomal University, Dera Ismail Khan 29220, Pakistan

Mathematics, 2022, vol. 10, issue 5, 1-30

Abstract: As the amount of historical data available in the legal arena has grown over time, industry specialists are driven to gather, compile, and analyze this data in order to forecast court case rulings. However, predicting and justifying court rulings while using judicial facts is no easy task. Currently, previous research on forecasting court outcomes using small experimental datasets yielded a number of unanticipated predictions utilizing machine learning (ML) models and conventional methodologies for categorical feature encoding. The current work proposes forecasting court judgments using a hybrid neural network model, namely a long short-term memory (LSTM) network with a CNN, in order to effectively forecast court rulings using historic judicial datasets. By prioritizing and choosing features that scored the highest in the provided legal data set, only the most pertinent features were picked. After that, the LSTM+CNN model was utilized to forecast lawsuit verdicts. In contrast to previous related experiments, this composite model’s testing results were promising, showing 92.05 percent accuracy, 93 percent precision, 94 percent recall, and a 93 percent F1-score.

Keywords: court judgment prediction; judicial data; deep learning; neural networks; feature selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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