Workforce Wellness Intelligence through Machine Learning
DOI:
https://doi.org/10.32628/IJSRSET2512321Keywords:
Employee health monitoring, smartwatches, machine learning, KNN, SVM, LinearRegressionAbstract
In recent decades, life-threatening diseases among employees have emerged as a significant global public health concern, particularly within many organizations. The advent of artificial intelligence presents an opportunity to forecast health-related parameters, thus facilitating more effective health management strategies. However, the practical applicability of machine learning (ML) techniques in predicting health parameters using data from low- and middle-income organizations remains limited. Utilizing machine learning (ML) techniques, research endeavors focus on addressing life-threatening diseases by analyzing health parameters extracted from smartwatch data. This approach harnesses advanced technologies to improve disease prediction and management. In conclusion, this study has culminated in the development of a comprehensive solution for Athma, Hypertension, Diabetes and hypoxia prediction and management. The successful development of an Android mobile app and an internet-based framework has shown promise, enabling users to input a range of health metrics and receive real-time forecasts for conditions like diabetes, HyperTension, and hypoxemia.This integrated platform leverages the power of machine learning models trained on health parameters derived from both basic health checkup tests and smartwatch data. By offering real-time insights and predictions, this solution empowers individuals to proactively manage their health and make informed decisions about diabetes prevention and control.
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