Development of Workforce Diseases Detection System Using Machine Learning Models

Health Status is one of the important parameters determining workers’ productivity and, therefore, significantly affects the nation’s workforce. This research work develops a workforce disease predictive system using Machine Learning Models. The models were trained using the five different machine learning algorithms, which include Neural Networks, Random Forest Classifier, XGBoost, Multilayer Perceptron, K-Nearest Neighbour, and uses an insertion sort algorithm for the second tier, which sorts the results to analyse and select the most likely disease for each of the workers. Hence, a system is developed which is capable of detecting such workforce diseases. Python programming language with Scikit Learn and TensorFlow Library was used as an instrument for implementation. The system is more accurate and unique in predicting workforce diseases than other existing methods or techniques