Automated Comparative Predictive Analysis of Deception Detection in Convicted Offenders Using Polygraph with Random Forest, Support Vector Machine, and Artificial Neural Network Models

Dana RAD, Csaba KISS, Nicolae PARASCHIV, Valentina Emilia BALAS

Abstract

This paper provides a thorough comparative review of deception detection techniques employed for a sample of 400 convicted offenders. It focuses on the utilization of polygraph sensor data as input variables for predicting deception, which are assessed against manual scoring by experts. Three advanced machine learning models, namely Random Forest Regression (RFR), Support Vector Machine (SVM) Regression, and Neural Network Regression (NNR), were employed with the purpose of analysing their predictive efficacy in identifying deception based on physiological responses captured by polygraph sensors. The obtained results indicate that all three algorithms exhibited varying degrees of effectiveness in predicting deceptive behavior. The Random Forest Regression algorithm achieved a Mean Squared Error (MSE) of 0.893 and a coefficient of determination (R2) of 0.091, which highlights its ability to discern key physiological indicators related to deceptive behavior. The Support Vector Machine Regression algorithm showed a competitive performance with a MSE of 0.98 and a R2 value of 0.159, which underscores its capability to model non-linear relationships in the context of high-dimensional data. However, the Neural Network Regression algorithm proved to be the best model, with a MSE of 0.894 and a significantly higher R2 value of 0.113. This model`s capacity to capture the complex relationships in the context of physiological data allowed it to surpass both RFR and SVM, which indicates its potential for a precise and reliable deception detection. This study provides valuable insights into the advancement of forensic applications with regard to deception detection technologies. Its findings suggest that the Neural Network Regression algorithm, due to its ability to learn complex patterns and relationships related to physiological data, stands out as an optimal choice for accurately identifying deceptive behavior.

Keywords

Deception detection, Polygraph sensor data, Machine Learning models, Predictive efficacy, Neural Network Regression, Random Forest Regression, Support Vector Machine Regression.

View full article