Smart Employee Monitoring System: Machine Learning-Driven Workforce Analytics and Risk Prediction

Abstract

Employee attrition, burnout, and low engagement can affect workforce stability and organizational performance. This paper presents a Smart Employee Monitoring System (SEMS) that combines machine learning, explainable AI, and natural language processing to support employee-related analysis. The system evaluates employee information to predict attrition risk and identify possible burnout conditions using multiple factors such as work patterns, job satisfaction, and stress indicators. Six machine learning algorithms Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Naive Bayes, and K-Nearest Neighbors are used for attrition prediction, with Random Forest achieving approximately 92% accuracy in the project evaluation. The system also uses SHAP-based explanations to show the factors influencing predictions and TextBlob-based sentiment analysis to analyze employee feedback. A role-based architecture provides separate interfaces for Admin, HR, and Employee users. The application was developed using a Flask backend, React frontend, and database-supported employee management modules. Testing covered authentication, APIs, frontend functions, database operations, and machine learning components, with 28 test cases successfully completed. The proposed system provides a unified approach for analyzing workforce risks and supporting data-informed HR decisions.

Introduction

The Smart Employee Monitoring System (SEMS) is a proposed HR analytics platform designed to help organizations proactively monitor employee attrition, burnout, wellbeing, and workplace sentiment. Traditional HR systems mainly store employee information and generate reports, but they often provide limited support for identifying employees who may be at risk of leaving or experiencing workplace difficulties.

SEMS addresses this limitation by integrating machine learning, Explainable AI (XAI), Natural Language Processing (NLP), and employee analytics into a single platform. It uses employee information such as performance, attendance, tenure, job satisfaction, working hours, and stress indicators to identify potential attrition and wellbeing risks.

Main Objectives

The system aims to:

• Predict employee attrition risk using multiple machine learning algorithms.

• Detect burnout and wellbeing risks using factors such as workload, stress, satisfaction, attendance, and performance.

• Analyze employee feedback using NLP-based sentiment analysis to classify opinions as positive, neutral, or negative.

• Use SHAP Explainable AI to show the factors influencing attrition predictions.

• Provide role-based dashboards and insights for Admin, HR, and Employee users.

Literature Review and Research Gap

Previous research has separately investigated employee attrition prediction, explainable AI, burnout detection, sentiment analysis, employee wellbeing, and workforce planning. Machine learning and XAI have demonstrated potential for predicting turnover and explaining the factors behind predictions.

However, the reviewed studies generally focus on one or a few HR analytics functions rather than combining them into a single operational platform. The identified research gap is therefore the lack of an integrated system that simultaneously provides:

• Attrition prediction

• Burnout detection

• Sentiment analysis

• Employee wellbeing monitoring

• Explainable AI

• Attendance and performance analytics

• Role-based HR dashboards

SEMS is proposed to address this integration gap.

Methodology

The system follows an integrated data-processing and machine-learning approach:

1. Data preprocessing: Employee data is cleaned, numerical variables are standardized using StandardScaler, categorical variables are one-hot encoded, and the dataset is divided into 80% training and 20% testing data. Five-fold cross-validation is used for evaluation.

2. Attrition prediction: Six algorithms are compared:

   • Decision Tree

   • Random Forest

   • Logistic Regression

   • Support Vector Machine

   • Naive Bayes

   • K-Nearest Neighbors

   The text reports that Random Forest achieved approximately 92% accuracy and was selected for attrition-risk prediction.

3. Explainable AI: SHAP is used to identify which employee characteristics contribute to an individual attrition prediction, helping HR users interpret model outputs rather than relying only on a risk label.

4. Burnout detection: Working hours, job satisfaction, and stress-related indicators are analyzed to categorize employees into Critical, High, Moderate, or Low burnout-risk levels.

5. Sentiment analysis: NLP is used to examine employee feedback and identify positive, neutral, or negative sentiment.

Conclusion

This research successfully developed and validated a Smart Employee Monitoring System integrating machine learning, explainable AI, and multi-dimensional risk assessment for comprehensive workforce analytics. The implementation demonstrates technical feasibility of predictive HR systems achieving high accuracy while maintaining interpretability through SHAP-based feature importance analysis. Testing validated all major components with 100% pass rate and demonstrated performance characteristics acceptable for real-world use.

References

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Copyright

Copyright © 2026 Trupti Panigrahi, Dr. V. Aruna. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.