Development of a Biometric Based Employment Tracking System (ETS) Using Machine Learning Approach

This study presents the development, implementation, and validation of two intelligent systems: an Unemployment Prediction Model and an Employment Tracking System (ETS), aimed at enhancing workforce planning and mitigating employment fraud in Nigeria. The unemployment prediction model was developed using linear regression and Feed Forward Neural Network (FFNN) techniques, trained on NYSC data of higher education graduates over five years. The ETS was developed using an FFNN classification model trained on fingerprint data from the Federal Ministry of Labour, Employment, and Productivity (FMLEP). The model was evaluated using Mean Square Error (MSE), Receiver Operating Characteristics (ROC), and confusion matrix andthe system implementation result achieved an average classification accuracy of 97.16% and a ROC of 0.9777, indicating a high capability in accurately detecting employed individuals. Initial results from the linear regression model showed a regression value (R2) of 0.88 but suffered from high Root Mean Square Error (RMSE), indicating poor reliability. Consequently, the FFNN was reconfigured for regression using a neural network time series application, resulting in improved performance with a regression value of 0.99624 and a near-zero RMSE of 0.050165. Validation through tenfold cross-validation confirmed the robustness of both models, with the FFNN outperforming linear regression by 20.4% in prediction accuracy. Furthermore, integration of the ETS enabled real-time identification of previously employed individuals through fingerprint matching, effectively preventing multiple government job applications using fraudulent identities. The results from the system demonstrated that FFNN-based models offer superior performance in both predictive and classification tasks within employment analytics. The integration of these systems into labour market governance provides a promising approach to data-driven decision-making, fraud prevention, and enhanced workforce transparency.